This is an open assessment looking at potential health effects of a national fish promotion program in Finland. The details of the assessment are described at Opasnet. This file contains the R code to run the assessment model.

Knit to html for best performance.

Calculation is based on BONUS GOHERR project and its fish health benefit-risk assessment.

What needs to be done for PFAS assessment?

  1. Amount should be gender and age-specific, because we need to worry about young mothers. Solution: take KKE amounts of fish, and scale those with the Goherr subgroup-specific proportions. DONE. Not age-specific atm.
  2. Amounts should reflect the actual fish consumption in Porvoo. Solution: postpone and use national statistics. DONE
  3. Infant’s dioxin concentration module must be added. Solution: Use Goherr model for expo_indir DONE
  4. The module must be updated to match PFAS as well. Solution: update the module to contain column Exposure_agent. Change body fat parameter to volume of distribution. DONE but not checked.
  5. Find why expo_indir is so much higher than EFSA toxicokinetic assessment. Make an alternative model.
  6. Add ERF for PFAS (sum of PFOS, PFHxS, PFOA, PFNA). Solution: Make a new page for ERF of PFAS and add that to adjusted ERFs. First immunology; postpone cholesterol and low birth weight and liver toxicity. DONE
  7. Add case burdens for PFAS outcomes. Solution: give rough estimates for immunology to get started. DONE
  8. Add PFAS concentrations to data. Solution: Look at Porvoo concentrations first and make conc_pfas; combine that with conc_vit. DONE
  9. Add dioxin and MeHg concentrations. DONE
  10. Now that the model runs technically, look through each part to check that it makes sense.
  11. If PAF contains some inputs with Age and some without Age (implying that they apply to any age group), only the explicit age groups remain in the end. Solution: Add RRorig to compensate for this. DONE
  12. TEQ and MeHg are missing from dose. Check why and correct. DONE
  13. The proportions of All-cause mortality and CHD2 mortality are 3:1 in Kotimaisen kalan edistämisohjelma but 3:2 in this analysis. Why?
  14. In exposure, ALA + DHA < Omega3. Why?
  15. BoDattr: age group 45 - 50 is way smaller than any other group. Why? Solution: it’s the misplaces 5-9-year age group. DONE
  16. conc_mehg does not contain Iter although it is probabilistic. Solution: Update formula. DONE
  17. TEQ response for cancer morbidity is way too large. Why? Is there a unit problem? Let’s look at age group 45-49. BoD 3800, BoDattr 970, dose (Female) 188, CSF 0.0005, incidence 0.00657, population (Female) 154000. This makes case burden = BoD / I / P = 3800/0.00657/154000 = 3.7 DALY/case which is ten times higher than Goherr case burden 0.39 DALY/case. The reason is that the former is about life-time exposure while the latter is per one-year exposure, which is the correct way. The current PAF implies that 21 and 31 % of cancers in females and males, respectively are caused by TEQ in all age groups. That’s an artifact also from the lifetime vs yearly mismatch.
  18. MeHg exposure is ca. 2 ug/d when you would assume 1 mg/kg * 0.03 kg/d = 30 ug/d. Where is the difference?
  19. Sperm concentration and dental defect show fairly large BoD. This is because the ERFs have exposure units pg/g, while the exposure has pg/d. Solution: implement the expo_dir submodule.
  20. Why is average fish so much higher with PFAS concentration than other fish species and the expectation? Because individual samples are accidentally summed, not bootstrapped. Solution: update formula for conc_pfas.
objects.latest("Op_en2261", code_name="RR2") # RR on page [[Health impact assessment]]
## Loading objects:
##   RR
RRorig <- RR@formula

RR@formula <- function(...) {
  out <- RRorig()
  out <- out * Ovariable(
    output=data.frame(Age = ages, Result=1),
    marginal=c(TRUE,FALSE)
  )
  return(out)
}
# This code was forked from https://github.com/jtuomist/fishhealth/blob/master/fishhealth.Rmd
# This code was previously forked from code Op_fi5923/model on page [[Kotimaisen kalan edistämisohjelma]]
# The code was even more previously forked from Op_fi5889/model on page [[Ruori]] and Op_en7748/model on page [[Goherr assessment]]

dat <- opbase.data("Op_fi5932", subset="Malliparametrit")[-1] # [[PFAS-yhdeisteiden tautitaakka]]
dec <- opbase.data("Op_fi5932", subset="Decisions")[-1]
DecisionTableParser(dec)

CTable <- opbase.data("Op_fi5932",subset="CollapseMarginals")
#for(i in 1:ncol(CTable)) {CTable[[i]] <- as.character(CTable[[i]])} # The default is currently character, not factor
CollapseTableParser(CTable)

cat("Laskennassa käytetty data.\n")
## Laskennassa käytetty data.
dat
cat("Tarkastellut päätökset.\n")
## Tarkastellut päätökset.
dec
cat("Aggregoidut marginaalit.\n")
## Aggregoidut marginaalit.
CTable
dummy <- Ovariable("dummy",data=data.frame(Age="dummy", Fish="dummy", Compound="dummy", Area="dummy", Result=1)) # Keep these columns marginals

fish_proportion <- Ovariable( # How population subgroups eat fish differently
  "fish_proportion",
  data = data.frame(prepare(dat,"fish proportion",c("Type","Exposure_agent","Response","Unit"))),
  unit="proportion of the mean")

total_amount <- Ovariable(
  "total_amount",
  data=prepare(dat, "amount", c("Type","Response","Exposure_agent","Unit")),
  unit="M kg/a")

amount <- Ovariable(
  "amount",
  dependencies = data.frame(Name=c("total_amount", "fish_proportion")),
  formula = function(...) {
    amount <- total_amount
      # Filleted weight, i.e. no loss.
    amount <- amount * 1000 / 5.52 /365.25 
      # M kg/a per 5.52M population --> g/d per average person.
    amount <- amount * fish_proportion
      # fish_proportion tells the relative amount in each subgroup
  
    # Match KKE-classification in amount with Fineli classification
    tmp <- Ovariable(
      output = data.frame(
        Kala = c("Kasvatettu", "Kaupallinen", "Kirjolohi", "Silakka", "Vapaa-ajan", "Muu tuonti", "Tuontikirjolohi", "Tuontilohi"),
        Fish = c("Whitefish", "Average fish","Rainbow trout", "Herring", "Average fish", "Average fish", "Rainbow trout", "Salmon"),
        Result = 1
      ),
      marginal = c(TRUE, TRUE, FALSE)
    )
    
    amount <- amount * tmp
    
    return(amount)
  },
  unit="g/d"
)
# Exposure:To child and To eater not needed, because dioxins are not (yet) included

population <- Ovariable(
  "population", 
  data=prepare(dat, "population", c("Type", "Exposure_agent", "Response","Unit")),
  unit = "#"
)
population@data$Age <- factor(population@data$Age, levels=levels(ages))

incidence <- Ovariable(
  "incidence",
  data = prepare(dat,"incidence",c("Type","Exposure_agent","Unit")),
  unit="1/person-year")
#incidence@data$Age[is.na(incidence@data$Age)] <- ""

case_burden <- Ovariable(
  "case_burden",
  data = prepare(dat,"case burden",c("Type", "Exposure_agent","Unit")),
  unit="DALY/case")

ERFchoice <- Ovariable(
  "ERFchoice",
  data = 
    prepare(dat, "ERFchoice", c("Unit", "Type"))
)

InpBoD <- EvalOutput(Ovariable( # Evaluated because is not a dependency but an Input
  "InpBoD",
  data = prepare(dat, "BoD", c("Type","Exposure_agent","Unit")),
  unit="DALY/a"
))
InpBoD$Response[InpBoD$Response=="All causes"] <- "All-cause mortality"
InpBoD$Response[InpBoD$Response=="Depressive disorders"] <- "Depression"
InpBoD$Response[InpBoD$Response=="Neoplasms"] <- "Cancer morbidity yearly"
InpBoD$Response[InpBoD$Response=="Respiratory infections and tuberculosis"] <- "Immunosuppression" # Infections of 0-9-year-olds are assumed to represent the background BoD of immunosuppressive effect of PFAS
InpBoD$Response[InpBoD$Response=="Cardiovascular diseases"] <- "CHD2 mortality"
conc_vit <- Ovariable(
  "conc_vit",
  ddata = "Op_en1838", # [[Concentrations of beneficial nutrients in fish]]
  subset = "Fineli data for common fish species",
  unit="ALA mg/g, DHA mg/g, Fish g/g, Omega3 mg/g, Vitamin D µg/g f.w. after adjustment"
)
  df = conc_vit@data
  df$Nutrient[df$Nutrient=="D-vitamiini (µg)"] <- "Vitamin D"
  df$Nutrient[df$Nutrient=="rasvahapot n-3 moni-tyydyttymättömät (g)"] <- "Omega3"
  df$Nutrient[df$Nutrient=="rasvahappo 18:3 n-3 (alfalinoleenihappo) (mg)"] <- "ALA"
  df$Nutrient[df$Nutrient=="rasvahappo 22:6 n-3 (DHA) (mg)"] <- "DHA"
  df$Nutrient[df$Nutrient=="proteiini (g)"] <- "Fish"
  df$conc_vitResult[df$Nutrient=="Fish"] <- "1"
  df <- dropall(df[df$Nutrient %in% c("Vitamin D", "Omega3", "ALA", "DHA", "Fish") , ])
conc_vit@data <- df

######## Concentration of PFAS

# Data from EU-kalat3 (Finland excl Vanhankaupunginlahti): # pg/g fresh weight
#       POP     mean       sd      min   Q0.025   median   Q0.975      max
# 2.5% PFOS 2055.757 1404.045 305.0399 330.1365 1533.269 5029.697 5814.935

# Data from EU-kalat3 (Vanhankaupunginlahti, Helsinki) # ng/g f.w.
#      POP   mean       sd      min   Q0.025   median   Q0.975      max
#2.5% PFOS 14.428 11.94542 1.499441 1.607789 15.64988 35.32517 38.91994

conc_eukalat <- EvalOutput(Ovariable(
  "conc_eukalat",
  data = data.frame(
    Area = c("Suomi","Helsinki"),
    Compound="PFOS",
    Result=c("2.056 (3.301 - 5.030)", "14.428 (1.499 - 35.325)")),
  unit="ng/g fresh weight"
))

conc_pfas_raw <- Ovariable(
  "conc_pfas_raw",
  ddata="Op_fi5932",subset="PFAS concentrations",
  unit="ng/g f.w.")

conc_pfas <- Ovariable(
  "conc_pfas",
  dependencies = data.frame(Name="conc_pfas_raw"),
  formula = function(...) {
    out <- conc_pfas_raw
    out$Source <- NULL
    out$Area <- "Porvoo"
    out <- expand_index(out, list(Fish=list(Perch=c(
      "Average fish", "Pike","Rainbow trout","Roach", "Salmon", "Vendace", "Whitefish"))))
    out <- out[sample(nrow(out@output)),]
    out <- out[!duplicated(out@output[intersect(colnames(out@output),c("Fish","Compound","Iter"))]),]
    out@marginal <- c(TRUE, TRUE, FALSE, TRUE, TRUE)
    return(out)
  },
  unit="ng/g fresh weight"
)

#sum_pfas <- oapply(conc_pfas, cols=c("Kala","Compound"), FUN=sum)
#tmp <- conc_pfas / sum_pfas
#summary(tmp, marginals="Compound")
#
## This tells that PFOS consists of 71 - 97 % of the four key PFAS, while PFOA, PFNA, and PFHxS consist of 
# 0 - 10 %, 2 - 18 %, and 0 - 9 %, respectively.
# Even if we included the next most abundant congeners, i.e. PFDA and PFUnA, the overall picture would not change.

conc <- Ovariable(
  "conc",
  dependencies = data.frame(
    Name=c("conc_vit", "conc_pfas","conc_pcddf","conc_mehg"),
    Ident = c(NA,NA,"Op_en3104/pcddf_mean","Op_en4004/conc_mehg_allfish")),
  formula = function(...){
    conc_vit <- oapply(conc_vit, cols=c("Kala", "Adjust"),FUN=mean)
    colnames(conc_vit@output)[colnames(conc_vit@output)=="Nutrient"] <- "Compound"

#    conc_pfas <- oapply(conc_pfas, cols=c("Obs","Area"), FUN=mean) # Redundant because handled already
    conc_pfas$Compound[conc_pfas$Compound %in% c("PFOA","PFNA","PFHxS","PFOS")] <- "PFAS"
    conc_pfas <- oapply(conc_pfas, cols="Area", FUN=sum) # Works because only one area
    
    conc_pcddf$Compound <- "TEQ"
    
    conc_mehg <- oapply(conc_mehg[conc_mehg$Area=="Porvoo",], NULL, mean, c("Area","Kala")) # UPDATE FOR PROBABILISTIC APPROACH
    conc_mehg$Compound <- "MeHg"
    
    out <- OpasnetUtils::combine(conc_vit, conc_pfas, conc_pcddf, conc_mehg)
    return(out)
  }
)

#conc <- EvalOutput(conc, verbose=TRUE)
#View(conc@output)
#View(conc_pcddf@output)
#View(conc_mehg@output)
###################################################################
# Code copied from http://en.opasnet.org/w/Goherr_assessment#

mc2dparam<- list(
  N2 = 10, # Number of iterations in the new Iter
  strength = 50, # Sample size to which the fun is to be applied. Resembles number of observations
  run2d = FALSE, # Should the mc2d function be used or not?
  info = 1, # Ovariable that contains additional indices, e.g. newmarginals.
  newmarginals = c("Exposure"), # Names of columns that are non-marginals but should be sampled enough to become marginals
  method = "bootstrap", # which method to use for 2D Monte Carlo? Currently bootsrap is the only option.
  fun = mean # Function for aggregating the first Iter dimension.
)

if(FALSE) {
## Exposure with background exposure but without mother's exposure to child

expo_dir <- Ovariable(
  "expo_dir",
  dependencies=data.frame(Name=c("amount","conc","expo_bg")),
  formula = function(...) {
    out <- conc[conc$Exposure_agent=="TEQ",] * 0 + 1
    out$Exposure_agent <- "Fish"
    out <- combine(conc, out, name="conc")
    out <- oapply(amount * out, cols="Fish", FUN=sum)
    out <- Ovariable(output = data.frame(
      Exposcen = c("BAU", "No exposure"),
      Result = c(1, 0)
    ), marginal=c(TRUE,FALSE)) * out + expo_bg
    out$Exposure <- as.factor(
      ifelse(
        out$Exposure_agent %in% c("DHA", "MeHg"),
        "To child",
        "To eater"
      )
    )
    return(out)
  },
  unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d; Fish: g /d"
)

## Background-exposure to vitamin D and omega-3
addexposure <- Ovariable(
  "addexposure",
  ddata = "Op_en7748", # [[Benefit-risk assessment of Baltic herring and salmon intake]]
  subset = "Background exposure",
  unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d"
)

# Should the background be specific for gender and country? At the moment it is.
expo_bg <- Ovariable(
  "expo_bg",
  dependencies = data.frame(Name="addexposure","info"),
  formula = function(...) {
    out <- addexposure
    
    # Empty values ("") in indices must be replaced by NA so that Ops works correctly.
    levels(out$Gender)[levels(out$Gender) == ""] <- NA
    levels(out$Country)[levels(out$Country) == ""] <- NA
    levels(out$Exposure_agent)[levels(out$Exposure_agent) == ""] <- NA
    out@output <- fillna(out@output, c("Country", "Gender", "Exposure_agent"))
    
    temp1 <- out[out$Exposure_agent %in% c("PCDDF","PCB") , ]
    temp1 <- oapply(temp1, cols = "Exposure_agent", FUN = sum)
    temp1$Exposure_agent <- "TEQ"
    
    temp2 <- out[out$Exposure_agent %in% c("EPA", "DHA") , ]
    temp2 <- oapply(temp2, cols = "Exposure_agent", FUN = sum)
    temp2$Exposure_agent <- "Omega3"
    
    out <- OpasnetUtils::combine(out, temp1, temp2)
    out <- unkeep(out * info, prevresults = TRUE, sources = TRUE)
    
    return(out)
  },
  unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d"
)

# Stores non-marginal columns for further use.
info <- Ovariable(
  "info",
  dependencies = data.frame(Name = c("jsp")),
  formula = function(...) {
    out <- jsp
    out$Group <- factor(
      paste(out$Gender, out$Ages),
      levels = c("Female 18-45", "Male 18-45", "Female >45", "Male >45")
    )
    out$Country <- factor(out$Country, ordered=FALSE)
    out <- unique(out@output[c("Iter","Country","Group","Gender","Row")])
    out$Result <- 1
    return(out)
  }
)

} # END IF
############################### Code from Goherr assessment ends

exposure <- Ovariable(
  "exposure",
  dependencies = data.frame(
    Name = c(
      "expo_dir", # direct exposure, i.e. the person eats or breaths the exposure agent themself
      "expo_indir", # indirect exposure, i.e. the person (typically fetus or infant) is exposed via someone else (mother)
      "mc2d" # 2D Monte Carlo function
    ),
    Ident = c(
      NA,
      "Op_en7797/expo_indir2", # [[Infant's dioxin exposure]] # expo_indir
      "Op_en7805/mc2d") # [[Two-dimensional Monte Carlo]]
  ),
  formula = function(...) {
#    expo_indir$Exposure <- NULL
    expo_indir <- expo_indir[expo_indir$Gender =="Female",]
    expo_indir$Gender <- NULL
    
    expo_dir$Exposure <- "Direct"
    
    out <- OpasnetUtils::combine(expo_dir, expo_indir)
    out <- unkeep(out, "Source.1", sources=TRUE)
    out <- mc2d(out)
    return(out)
  },
  unit = "PCDDF, PCB, TEQ: (To eater: pg /day; to child: pg /g fat); Vitamin D, MeHg: µg /day; DHA, EPA, Omega3: mg /day"
)

expo_dir <- Ovariable(
  "expo_dir",
  dependencies=data.frame(Name=c("amount","conc")),
  formula = function(...) {
    out <- amount * conc
    colnames(out@output)[colnames(out@output)=="Compound"] <- "Exposure_agent"
    return(out)
  },
  unit = "PCDDF, PCB, TEQ: pg /day; Vitamin D, MeHg: µg /day; DHA, EPA, Omega3: mg /day"
)
objects.latest("Op_en2261",code_name="BoDattr2") # [[Health impact assessment]]
## Loading objects:
##   BoDattr
tryCatch(BoDattr <- EvalOutput(BoDattr, verbose=TRUE))
##  Evaluating BoDattr ...
## 
##  - BoD fetched successfully!
## 
##  - PAF fetched successfully!
## - Evaluating BoD ...
## - - Evaluating incidence ...
## 
##  done(0.01 secs)!
## - - Checking incidence marginals ... Response, Age, incidenceSource recognized as marginal(s).
## Loading required package: reshape2
## 
## Attaching package: 'reshape2'
## The following object is masked from 'package:tidyr':
## 
##     smiths
## - - Processing incidence decisions ... done!
## - - Evaluating case_burden ...
## 
##  done(0 secs)!
## - - Checking case_burden marginals ... Response, case_burdenSource recognized as marginal(s).
## - - Processing case_burden marginal collapses ... done!
## - - Evaluating population ...
## 
##  done(0 secs)!
## - - Checking population marginals ... Gender, Age, populationSource recognized as marginal(s).
## 
## - done(0.3 secs)!
## - Checking BoD marginals ... Response, Age, incidenceSource, Adjust, Gender, populationSource, BoDSource recognized as marginal(s).
## - Processing BoD inputs ... done!
## - Processing BoD marginal collapses ...
## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying BoD, found NAs in indices: Adjust, InpBoDSource. They were
## automatically filled using fillna, which may result in a multiplied population.
## Please check your ovariable before using oapply.
##  done!
## - Evaluating PAF ...
## 
##  - - dose fetched successfully!
## 
##  - - ERF fetched successfully!
## 
##  - - frexposed fetched successfully!
## 
##  - - P_illness fetched successfully!
## 
##  - - sumExposcen fetched successfully!
## 
##  - - mc2d fetched successfully!
## - - Evaluating dose ...
## 
##  - - - BW fetched successfully!
## - - - Evaluating exposure ...
## 
##  - - - - expo_indir fetched successfully!
## - - - - Evaluating expo_dir ...
## - - - - - Evaluating amount ...
## - - - - - - Evaluating total_amount ...
## 
##  done(0 secs)!
## - - - - - - Checking total_amount marginals ... Kala, Scenario, total_amountSource recognized as marginal(s).
## - - - - - - Evaluating fish_proportion ...
## 
##  done(0 secs)!
## - - - - - - Checking fish_proportion marginals ... Gender, fish_proportionSource recognized as marginal(s).
## 
## ----- done(0.16 secs)!
## - - - - - Checking amount marginals ... Kala, Scenario, total_amountSource, Gender, fish_proportionSource, Fish, amountSource recognized as marginal(s).
## - - - - - Evaluating conc ...
## 
##  - - - - - - conc_pcddf fetched successfully!
## 
##  - - - - - - conc_mehg fetched successfully!
## - - - - - - Evaluating conc_vit ...
## 
##  done(0.02 secs)!
## - - - - - - Checking conc_vit marginals ... Kala, Fish, Nutrient, conc_vitSource recognized as marginal(s).
## - - - - - - Processing conc_vit decisions ... done!
## - - - - - - Evaluating conc_pfas ...
## - - - - - - - Evaluating conc_pfas_raw ...
## 
##  done(0.03 secs)!
## - - - - - - - Checking conc_pfas_raw marginals ... Fish, Compound, conc_pfas_rawSource recognized as marginal(s).
## 
## ------ done(0.11 secs)!
## - - - - - - Checking conc_pfas marginals ... Fish, Compound, conc_pfas_rawSource, Area, conc_pfasSource recognized as marginal(s).
## - - - - - - Processing conc_pfas marginal collapses ... done!
## - - - - - - Evaluating conc_pcddf ...
## 
##  - - - - - - - TEF fetched successfully!
## - - - - - - - Evaluating TEF ...
## 
##  - - - - - - - - TEFversion fetched successfully!
## - - - - - - - - Evaluating TEFraw ...
## 
##  done(0.01 secs)!
## - - - - - - - - Checking TEFraw marginals ... Group, Compound, TEFversion, TEFrawSource recognized as marginal(s).
## 
## ------- done(24.89 secs)!
## - - - - - - - Checking TEF marginals ... Compound, Group, TEFversion, TEFrawSource, TEFSource recognized as marginal(s).
## 
## ------ done(1.04 mins)!
## - - - - - - Checking conc_pcddf marginals ... Fish, conc_pcddfSource recognized as marginal(s).
## - - - - - - Evaluating conc_mehg ...
## 
##  - - - - - - - Hg fetched successfully!
## 
## ------ done(24.05 secs)!
## - - - - - - Checking conc_mehg marginals ... Area, Kala, Fish, conc_mehgSource recognized as marginal(s).
## 
## ----- done(2.28 mins)!
## - - - - - Checking conc marginals ... Fish, Compound, concSource recognized as marginal(s).
## 
## ---- done(2.28 mins)!
## - - - - Checking expo_dir marginals ... Kala, Scenario, total_amountSource, Gender, fish_proportionSource, Fish, amountSource, Exposure_agent, concSource, expo_dirSource recognized as marginal(s).
## - - - - Evaluating expo_indir ...
## - - - - - Evaluating t0.5 ...
## 
##  done(0 secs)!
## - - - - - Checking t0.5 marginals ... Exposure_agent, t0.5Source recognized as marginal(s).
## - - - - - Evaluating f_ing ...
## 
##  done(0 secs)!
## - - - - - Checking f_ing marginals ... Exposure_agent, f_ingSource recognized as marginal(s).
## - - - - - Evaluating f_mtoc ...
## 
##  done(0 secs)!
## - - - - - Checking f_mtoc marginals ... Exposure_agent, f_mtocSource recognized as marginal(s).
## - - - - - Evaluating BF ...
## 
##  done(0 secs)!
## - - - - - Checking BF marginals ... Exposure_agent, BFSource recognized as marginal(s).
## 
## ---- done(0.24 secs)!
## - - - - Checking expo_indir marginals ... Kala, Scenario, total_amountSource, Gender, fish_proportionSource, Fish, amountSource, Exposure_agent, concSource, expo_dirSource, f_ingSource, t0.5Source, f_mtocSource, BFSource, Exposure, expo_indirSource recognized as marginal(s).
## - - - - Processing expo_indir marginal collapses ... done!
## 
## --- done(2.68 mins)!
## - - - Checking exposure marginals ... Kala, Scenario, Gender, Fish, Exposure_agent, Exposure, exposureSource recognized as marginal(s).
## - - - Processing exposure marginal collapses ...
## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying exposure, found NAs in indices: Gender. They were automatically
## filled using fillna, which may result in a multiplied population. Please check
## your ovariable before using oapply.
##  done!
## - - - Evaluating BW ...
## 
##  done(0 secs)!
## - - - Checking BW marginals ... BWSource recognized as marginal(s).
## 
## -- done(3.09 mins)!
## - - Checking dose marginals ... Scenario, Gender, Exposure_agent, Exposure, Scaling, exposureSource, BWSource, doseSource recognized as marginal(s).
## - - Processing dose marginal collapses ... done!
## - - Evaluating ERF ...
## 
##  - - - ERF_env fetched successfully!
## 
##  - - - ERF_omega3 fetched successfully!
## 
##  - - - ERF_mehg fetched successfully!
## 
##  - - - ERF_diox fetched successfully!
## 
##  - - - ERF_vit fetched successfully!
## 
##  - - - ERF_micr fetched successfully!
## 
##  - - - ERF_pfas fetched successfully!
## - - - Evaluating ERF_env ...
## 
##  done(0.02 secs)!
## - - - Checking ERF_env marginals ... Exposure_agent, Response, Subgroup, Exposure, ER_function, Scaling, Exposure_unit, Observation, ERF_envSource recognized as marginal(s).
## - - - Evaluating ERF_omega3 ...
## 
##  done(0 secs)!
## - - - Checking ERF_omega3 marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_omega3Source recognized as marginal(s).
## - - - Evaluating ERF_mehg ...
## 
##  done(0 secs)!
## - - - Checking ERF_mehg marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_mehgSource recognized as marginal(s).
## - - - Evaluating ERF_diox ...
## 
##  done(0 secs)!
## - - - Checking ERF_diox marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_dioxSource recognized as marginal(s).
## - - - Evaluating ERF_vit ...
## 
##  done(0 secs)!
## - - - Checking ERF_vit marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_vitSource recognized as marginal(s).
## - - - Evaluating ERF_micr ...
## 
##  done(0 secs)!
## - - - Checking ERF_micr marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_micrSource recognized as marginal(s).
## - - - Evaluating ERF_pfas ...
## 
##  done(0 secs)!
## - - - Checking ERF_pfas marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_pfasSource recognized as marginal(s).
## - - - Evaluating ERFchoice ...
## 
##  done(0 secs)!
## - - - Checking ERFchoice marginals ... Exposure_agent, Response, Scaling, Exposure, ER_function, ERFchoiceSource recognized as marginal(s).
## 
## -- done(2.77 mins)!
## - - Checking ERF marginals ... Exposure_agent, Response, Exposure, ER_function, Scaling, Observation, ERFSource recognized as marginal(s).
## - - Processing ERF marginal collapses ... done!
## - - Evaluating RR ...
## - - - Processing dose marginal collapses ... done!
## - - - Processing ERF marginal collapses ... done!
## 
## -- done(0.17 secs)!
## - - Checking RR marginals ... Exposure_agent, Response, ER_function, Scaling, ERFSource, Scenario, Gender, Exposure, doseSource, Age, RRSource recognized as marginal(s).
## - - Evaluating frexposed ...
## 
##  done(0 secs)!
## - - Checking frexposed marginals ... frexposedSource recognized as marginal(s).
## - - Evaluating P_illness ...
## 
##  done(0 secs)!
## - - Checking P_illness marginals ... Response, Illness, Age, P_illnessSource recognized as marginal(s).
## 
## - done(8.38 mins)!
## - Checking PAF marginals ... Exposure_agent, Response, ER_function, Scaling, ERFSource, Scenario, Gender, Exposure, doseSource, frexposedSource, Age, incidenceSource, Adjust, RRSource, PAFSource recognized as marginal(s).
## - Processing PAF marginal collapses ...
## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying PAF, found NAs in indices: Age, Adjust. They were automatically
## filled using fillna, which may result in a multiplied population. Please check
## your ovariable before using oapply.
##  done!
## 
##  done(9.23 mins)!
##  Checking BoDattr marginals ... Response, Age, Gender, Adjust, InpBoDSource, Exposure_agent, Scenario, PAFSource, BoDattrSource recognized as marginal(s).
oprint(summary(amount,"mean"))
##               Kala Scenario Gender          Fish       mean
## 1      Kaupallinen      BAU Female  Average fish  1.2697279
## 2       Muu tuonti      BAU Female  Average fish 11.1101191
## 3       Vapaa-ajan      BAU Female  Average fish  3.9282207
## 4      Kaupallinen      BAU   Male  Average fish  1.9045919
## 5       Muu tuonti      BAU   Male  Average fish 16.6651787
## 6       Vapaa-ajan      BAU   Male  Average fish  5.8923310
## 7          Silakka      BAU Female       Herring  0.6348640
## 8          Silakka      BAU   Male       Herring  0.9522959
## 9        Kirjolohi      BAU Female Rainbow trout  2.6584928
## 10 Tuontikirjolohi      BAU Female Rainbow trout  1.9045919
## 11       Kirjolohi      BAU   Male Rainbow trout  3.9877392
## 12 Tuontikirjolohi      BAU   Male Rainbow trout  2.8568878
## 13      Tuontilohi      BAU Female        Salmon  9.3642433
## 14      Tuontilohi      BAU   Male        Salmon 14.0463649
## 15      Kasvatettu      BAU Female     Whitefish  0.2380740
## 16      Kasvatettu      BAU   Male     Whitefish  0.3571110
oprint(summary(BoD,marginals=c("Age","Response"),"mean"))
##           Age                                          Response       mean
## 1       0 - 4                           Cancer morbidity yearly   309.5200
## 2     10 - 14                           Cancer morbidity yearly   347.0750
## 3     15 - 19                           Cancer morbidity yearly   414.9750
## 4     20 - 24                           Cancer morbidity yearly   569.5250
## 5     25 - 29                           Cancer morbidity yearly   795.4550
## 6     30 - 34                           Cancer morbidity yearly  1147.9850
## 7     35 - 39                           Cancer morbidity yearly  1679.1250
## 8     40 - 44                           Cancer morbidity yearly  2452.5650
## 9     45 - 49                           Cancer morbidity yearly  3849.3800
## 10      5 - 9                           Cancer morbidity yearly   345.5200
## 11    50 - 54                           Cancer morbidity yearly  6987.9000
## 12    55 - 59                           Cancer morbidity yearly 11237.3300
## 13    60 - 64                           Cancer morbidity yearly 16331.9100
## 14    65 - 69                           Cancer morbidity yearly 21899.6500
## 15    70 - 74                           Cancer morbidity yearly 25551.8600
## 16    75 - 79                           Cancer morbidity yearly 18015.2300
## 17    80 - 84                           Cancer morbidity yearly 14650.9150
## 18    85 - 89                           Cancer morbidity yearly  8413.0550
## 19    90 - 94                           Cancer morbidity yearly  3537.1750
## 20  Undefined      Dioxin recommendation tolerable daily intake   305.4067
## 21  Undefined Dioxin recommendation tolerable daily intake 2018   888.4560
## 22      0 - 4                                 Immunosuppression   295.9850
## 23      5 - 9                                 Immunosuppression   261.5350
## 24      0 - 4                         Loss in child's IQ points 16778.3774
## 25  Undefined                                          PFAS TWI  2776.4249
## 26      0 - 4                               Sperm concentration  4478.6700
## 27  Undefined                          Vitamin D recommendation   610.8135
## 28      0 - 4                           Yes or no dental defect   343.9619
## 29      0 - 4                               All-cause mortality  4867.4650
## 30    10 - 14                               All-cause mortality  1085.5450
## 31    15 - 19                               All-cause mortality  2965.9450
## 32    20 - 24                               All-cause mortality  5274.8450
## 33    25 - 29                               All-cause mortality  6263.5400
## 34    30 - 34                               All-cause mortality  6710.3100
## 35    35 - 39                               All-cause mortality  8275.6450
## 36    40 - 44                               All-cause mortality 10232.0150
## 37    45 - 49                               All-cause mortality 13691.2800
## 38      5 - 9                               All-cause mortality   931.1250
## 39    50 - 54                               All-cause mortality 21321.8650
## 40    55 - 59                               All-cause mortality 29861.9100
## 41    60 - 64                               All-cause mortality 39200.8750
## 42    65 - 69                               All-cause mortality 49295.5050
## 43    70 - 74                               All-cause mortality 59799.7000
## 44    75 - 79                               All-cause mortality 49428.5150
## 45    80 - 84                               All-cause mortality 51968.9150
## 46    85 - 89                               All-cause mortality 42765.3450
## 47    90 - 94                               All-cause mortality 26756.6400
## 48      0 - 4                                        Depression     0.4150
## 49    10 - 14                                        Depression   555.8850
## 50    15 - 19                                        Depression  1320.8250
## 51    20 - 24                                        Depression  1841.0150
## 52    25 - 29                                        Depression  1801.4650
## 53    30 - 34                                        Depression  1590.0200
## 54    35 - 39                                        Depression  1721.7250
## 55    40 - 44                                        Depression  1700.2800
## 56    45 - 49                                        Depression  1534.1700
## 57      5 - 9                                        Depression    76.4850
## 58    50 - 54                                        Depression  1718.9550
## 59    55 - 59                                        Depression  1767.1650
## 60    60 - 64                                        Depression  1676.7400
## 61    65 - 69                                        Depression  1561.5750
## 62    70 - 74                                        Depression  1426.2350
## 63    75 - 79                                        Depression   799.7000
## 64    80 - 84                                        Depression   581.2950
## 65    85 - 89                                        Depression   363.0750
## 66    90 - 94                                        Depression   186.1150
## 67      0 - 4                                    CHD2 mortality    53.6100
## 68    10 - 14                                    CHD2 mortality    75.3150
## 69    15 - 19                                    CHD2 mortality   145.3450
## 70    20 - 24                                    CHD2 mortality   254.1000
## 71    25 - 29                                    CHD2 mortality   437.6450
## 72    30 - 34                                    CHD2 mortality   636.4200
## 73    35 - 39                                    CHD2 mortality  1168.2600
## 74    40 - 44                                    CHD2 mortality  1977.5650
## 75    45 - 49                                    CHD2 mortality  3117.3100
## 76      5 - 9                                    CHD2 mortality    48.7800
## 77    50 - 54                                    CHD2 mortality  5476.2900
## 78    55 - 59                                    CHD2 mortality  8747.4650
## 79    60 - 64                                    CHD2 mortality 13077.9700
## 80    65 - 69                                    CHD2 mortality 18100.7500
## 81    70 - 74                                    CHD2 mortality 24549.2850
## 82    75 - 79                                    CHD2 mortality 23413.4850
## 83    80 - 84                                    CHD2 mortality 27899.3350
## 84    85 - 89                                    CHD2 mortality 25240.0350
## 85    90 - 94                                    CHD2 mortality 16630.6150
## 86    15 - 19                                     Breast cancer     2.0900
## 87    20 - 24                                     Breast cancer     7.7850
## 88    25 - 29                                     Breast cancer    36.8200
## 89    30 - 34                                     Breast cancer   145.1600
## 90    35 - 39                                     Breast cancer   263.6000
## 91    40 - 44                                     Breast cancer   433.3500
## 92    45 - 49                                     Breast cancer   704.9550
## 93    50 - 54                                     Breast cancer  1045.3250
## 94    55 - 59                                     Breast cancer  1341.8150
## 95    60 - 64                                     Breast cancer  1513.2150
## 96    65 - 69                                     Breast cancer  1649.3300
## 97    70 - 74                                     Breast cancer  1678.0350
## 98    75 - 79                                     Breast cancer  1232.0100
## 99    80 - 84                                     Breast cancer   950.8800
## 100   85 - 89                                     Breast cancer   578.1900
## 101   90 - 94                                     Breast cancer   296.1800
oprint(summary(BoDattr,marginals=c("Age","Response"),"mean"))
##          Age                                          Response          mean
## 1      0 - 4                           Cancer morbidity yearly  1.785242e+00
## 2    10 - 14                           Cancer morbidity yearly  1.956941e+00
## 3    15 - 19                           Cancer morbidity yearly  2.381629e+00
## 4    20 - 24                           Cancer morbidity yearly  3.274527e+00
## 5    25 - 29                           Cancer morbidity yearly  4.577121e+00
## 6    30 - 34                           Cancer morbidity yearly  6.480503e+00
## 7    35 - 39                           Cancer morbidity yearly  9.443872e+00
## 8    40 - 44                           Cancer morbidity yearly  1.368822e+01
## 9    45 - 49                           Cancer morbidity yearly  2.145437e+01
## 10     5 - 9                           Cancer morbidity yearly  1.951139e+00
## 11   50 - 54                           Cancer morbidity yearly  3.939245e+01
## 12   55 - 59                           Cancer morbidity yearly  6.458151e+01
## 13   60 - 64                           Cancer morbidity yearly  9.463669e+01
## 14   65 - 69                           Cancer morbidity yearly  1.275639e+02
## 15   70 - 74                           Cancer morbidity yearly  1.487741e+02
## 16   75 - 79                           Cancer morbidity yearly  1.042378e+02
## 17   80 - 84                           Cancer morbidity yearly  8.398121e+01
## 18 Undefined      Dioxin recommendation tolerable daily intake -2.165611e+03
## 19 Undefined Dioxin recommendation tolerable daily intake 2018  1.776912e+03
## 20     0 - 4                                 Immunosuppression  7.714128e+00
## 21     5 - 9                                 Immunosuppression  8.254486e+00
## 22     0 - 4                         Loss in child's IQ points  6.974660e+02
## 23 Undefined                                          PFAS TWI  1.370935e+03
## 24     0 - 4                               Sperm concentration  5.024148e+03
## 25 Undefined                          Vitamin D recommendation  6.108135e+02
## 26     0 - 4                           Yes or no dental defect  2.795378e+03
## 27     0 - 4                               All-cause mortality -3.940215e+02
## 28   10 - 14                               All-cause mortality -8.729181e+01
## 29   15 - 19                               All-cause mortality -2.510075e+02
## 30   20 - 24                               All-cause mortality -4.582433e+02
## 31   25 - 29                               All-cause mortality -5.472993e+02
## 32   30 - 34                               All-cause mortality -5.805901e+02
## 33   35 - 39                               All-cause mortality -7.164739e+02
## 34   40 - 44                               All-cause mortality -8.793680e+02
## 35   45 - 49                               All-cause mortality -1.166310e+03
## 36     5 - 9                               All-cause mortality -7.424681e+01
## 37   50 - 54                               All-cause mortality -1.808052e+03
## 38   55 - 59                               All-cause mortality -2.535812e+03
## 39   60 - 64                               All-cause mortality -3.319267e+03
## 40   65 - 69                               All-cause mortality -4.145066e+03
## 41   70 - 74                               All-cause mortality -4.980650e+03
## 42   75 - 79                               All-cause mortality -4.044850e+03
## 43   80 - 84                               All-cause mortality -4.134937e+03
## 44     0 - 4                                        Depression -7.647127e-02
## 45   10 - 14                                        Depression -9.937062e+01
## 46   15 - 19                                        Depression -2.347323e+02
## 47   20 - 24                                        Depression -3.276647e+02
## 48   25 - 29                                        Depression -3.226701e+02
## 49   30 - 34                                        Depression -2.865779e+02
## 50   35 - 39                                        Depression -3.110681e+02
## 51   40 - 44                                        Depression -3.073361e+02
## 52   45 - 49                                        Depression -2.766914e+02
## 53     5 - 9                                        Depression -1.387565e+01
## 54   50 - 54                                        Depression -3.090770e+02
## 55   55 - 59                                        Depression -3.166728e+02
## 56   60 - 64                                        Depression -2.997941e+02
## 57   65 - 69                                        Depression -2.791761e+02
## 58   70 - 74                                        Depression -2.551780e+02
## 59   75 - 79                                        Depression -1.425020e+02
## 60   80 - 84                                        Depression -1.024717e+02
## 61     0 - 4                                    CHD2 mortality -8.359787e+00
## 62   10 - 14                                    CHD2 mortality -1.170566e+01
## 63   15 - 19                                    CHD2 mortality -2.262663e+01
## 64   20 - 24                                    CHD2 mortality -3.966234e+01
## 65   25 - 29                                    CHD2 mortality -6.844631e+01
## 66   30 - 34                                    CHD2 mortality -9.964232e+01
## 67   35 - 39                                    CHD2 mortality -1.833859e+02
## 68   40 - 44                                    CHD2 mortality -3.106654e+02
## 69   45 - 49                                    CHD2 mortality -4.899542e+02
## 70     5 - 9                                    CHD2 mortality -7.579246e+00
## 71   50 - 54                                    CHD2 mortality -8.613391e+02
## 72   55 - 59                                    CHD2 mortality -1.375930e+03
## 73   60 - 64                                    CHD2 mortality -2.055932e+03
## 74   65 - 69                                    CHD2 mortality -2.841726e+03
## 75   70 - 74                                    CHD2 mortality -3.847268e+03
## 76   75 - 79                                    CHD2 mortality -3.660036e+03
## 77   80 - 84                                    CHD2 mortality -4.346514e+03
## 78   15 - 19                                     Breast cancer -4.624153e-01
## 79   20 - 24                                     Breast cancer -1.587543e+00
## 80   25 - 29                                     Breast cancer -7.326368e+00
## 81   30 - 34                                     Breast cancer -2.873340e+01
## 82   35 - 39                                     Breast cancer -5.218217e+01
## 83   40 - 44                                     Breast cancer -8.577757e+01
## 84   45 - 49                                     Breast cancer -1.395958e+02
## 85   50 - 54                                     Breast cancer -2.070689e+02
## 86   55 - 59                                     Breast cancer -2.658472e+02
## 87   60 - 64                                     Breast cancer -2.998331e+02
## 88   65 - 69                                     Breast cancer -3.267095e+02
## 89   70 - 74                                     Breast cancer -3.327787e+02
## 90   75 - 79                                     Breast cancer -2.441402e+02
## 91   80 - 84                                     Breast cancer -1.884558e+02
oprint(summary(BoDattr,marginals=c("Exposure_agent","Response"),"mean"))
##    Exposure_agent                                          Response
## 1             TEQ                           Cancer morbidity yearly
## 2             TEQ      Dioxin recommendation tolerable daily intake
## 3             TEQ Dioxin recommendation tolerable daily intake 2018
## 4            PFAS                                 Immunosuppression
## 5             DHA                         Loss in child's IQ points
## 6            MeHg                         Loss in child's IQ points
## 7            PFAS                                          PFAS TWI
## 8             TEQ                               Sperm concentration
## 9       Vitamin D                          Vitamin D recommendation
## 10            TEQ                           Yes or no dental defect
## 11           Fish                               All-cause mortality
## 12           Fish                                        Depression
## 13         Omega3                                    CHD2 mortality
## 14         Omega3                                     Breast cancer
##            mean
## 1     42.950665
## 2  -2165.611397
## 3   1776.911916
## 4      7.984307
## 5  -3292.013959
## 6   4686.945878
## 7   1370.934688
## 8   5024.148265
## 9    610.813471
## 10  2795.377581
## 11 -1771.969747
## 12  -228.525606
## 13 -1190.045495
## 14  -155.749901
oprint(summary(BoDattr,marginals=c("Gender","Response"),"mean"))
##    Gender                                          Response         mean
## 1  Female                           Cancer morbidity yearly    31.196086
## 2    Male                           Cancer morbidity yearly    54.705243
## 3  Female      Dioxin recommendation tolerable daily intake -2192.564681
## 4    Male      Dioxin recommendation tolerable daily intake -2138.658113
## 5  Female Dioxin recommendation tolerable daily intake 2018  1799.027431
## 6    Male Dioxin recommendation tolerable daily intake 2018  1754.796401
## 7  Female                                 Immunosuppression     5.957023
## 8    Male                                 Immunosuppression    10.011591
## 9  Female                         Loss in child's IQ points   542.752772
## 10   Male                         Loss in child's IQ points   852.179148
## 11 Female                                          PFAS TWI     0.000000
## 12   Male                                          PFAS TWI  2741.869376
## 13 Female                               Sperm concentration  4004.275815
## 14   Male                               Sperm concentration  6044.020715
## 15 Female                          Vitamin D recommendation   618.415679
## 16   Male                          Vitamin D recommendation   603.211263
## 17 Female                           Yes or no dental defect  2227.932428
## 18   Male                           Yes or no dental defect  3362.822734
## 19 Female                               All-cause mortality -1003.248948
## 20   Male                               All-cause mortality -2540.690546
## 21 Female                                        Depression  -235.343301
## 22   Male                                        Depression  -221.707910
## 23 Female                                    CHD2 mortality  -815.643510
## 24   Male                                    CHD2 mortality -1564.447480
## 25 Female                                     Breast cancer  -308.799252
## 26   Male                                     Breast cancer    -2.700550
oprint(summary(case_burden,"mean"))
##                                            Response        mean
## 1      Dioxin recommendation tolerable daily intake 0.001004988
## 2 Dioxin recommendation tolerable daily intake 2018 0.001004988
## 3                         Loss in child's IQ points 0.110000000
## 4                                          PFAS TWI 0.001004988
## 5                               Sperm concentration 2.500000000
## 6                          Vitamin D recommendation 0.001004988
## 7                           Yes or no dental defect 0.060000000
oprint(summary(conc,"mean"))
##                Fish  Compound        mean
## 1      Average fish       ALA  0.69000000
## 2             Bream       ALA  0.22000000
## 3           Herring       ALA  1.74000000
## 4              Pike       ALA  0.08000000
## 5     Rainbow trout       ALA  4.81000000
## 6             Roach       ALA  0.10000000
## 7            Salmon       ALA  7.96000000
## 8           Vendace       ALA  1.35000000
## 9         Whitefish       ALA  2.22000000
## 10     Average fish       DHA  2.54000000
## 11            Bream       DHA  2.73000000
## 12          Herring       DHA  5.86000000
## 13             Pike       DHA  0.30000000
## 14    Rainbow trout       DHA  7.57000000
## 15            Roach       DHA  2.87000000
## 16           Salmon       DHA  6.69000000
## 17          Vendace       DHA  3.00000000
## 18        Whitefish       DHA  3.94000000
## 19     Average fish      Fish  1.00000000
## 20            Bream      Fish  1.00000000
## 21          Herring      Fish  1.00000000
## 22             Pike      Fish  1.00000000
## 23    Rainbow trout      Fish  1.00000000
## 24            Roach      Fish  1.00000000
## 25           Salmon      Fish  1.00000000
## 26          Vendace      Fish  1.00000000
## 27        Whitefish      Fish  1.00000000
## 28     Average fish      MeHg  0.11413395
## 29            Bream      MeHg  0.63339772
## 30           Burbot      MeHg  1.02686230
## 31     Chaetiliidae      MeHg  0.01634649
## 32         Flounder      MeHg  0.04582576
## 33 Fourhorn sculpin      MeHg  0.64990137
## 34          Herring      MeHg  0.02680994
## 35            Perch      MeHg  0.14874312
## 36             Pike      MeHg  0.90310953
## 37       Pike-perch      MeHg  0.25432475
## 38            Roach      MeHg  0.26739658
## 39            Sprat      MeHg  0.03187976
## 40      White bream      MeHg  1.21073890
## 41        Whitefish      MeHg  0.06619502
## 42     Average fish    Omega3  7.00000000
## 43            Bream    Omega3  6.00000000
## 44          Herring    Omega3 24.00000000
## 45             Pike    Omega3  0.50000000
## 46    Rainbow trout    Omega3 18.00000000
## 47            Roach    Omega3  5.00000000
## 48           Salmon    Omega3 23.00000000
## 49          Vendace    Omega3 10.00000000
## 50        Whitefish    Omega3 10.00000000
## 51     Average fish      PFAS  8.26000000
## 52            Bream      PFAS  4.95000000
## 53              Eel      PFAS  7.89500000
## 54          Herring      PFAS  1.65500000
## 55            Perch      PFAS  5.12000000
## 56             Pike      PFAS  7.50500000
## 57       Pike-perch      PFAS  2.59500000
## 58    Rainbow trout      PFAS 27.74000000
## 59            Roach      PFAS 27.72000000
## 60           Salmon      PFAS  3.00000000
## 61          Vendace      PFAS  4.21000000
## 62        Whitefish      PFAS  4.22000000
## 63   Baltic herring       TEQ 90.08722156
## 64            Bream       TEQ  6.20175433
## 65           Burbot       TEQ  0.82549312
## 66              Cod       TEQ  1.67367981
## 67         Flounder       TEQ 15.36558024
## 68            Perch       TEQ  3.56897420
## 69             Pike       TEQ  2.14862962
## 70       Pike-perch       TEQ  2.70814568
## 71    River lamprey       TEQ  7.34301416
## 72            Roach       TEQ  0.72124877
## 73           Salmon       TEQ 20.00778296
## 74        Sea trout       TEQ  9.31778459
## 75            Sprat       TEQ  4.15456559
## 76          Vendace       TEQ  0.99253888
## 77        Whitefish       TEQ  5.74967872
## 78     Average fish Vitamin D  0.10500000
## 79            Bream Vitamin D  0.14000000
## 80          Herring Vitamin D  0.15600000
## 81             Pike Vitamin D  0.02100000
## 82    Rainbow trout Vitamin D  0.05100000
## 83            Roach Vitamin D  0.10000000
## 84           Salmon Vitamin D  0.06700000
## 85          Vendace Vitamin D  0.09400000
## 86        Whitefish Vitamin D  0.14400000
oprint(summary(dose,"mean"))
##    Scenario Gender Exposure_agent Exposure Scaling         mean
## 1       BAU Female            ALA   Direct      BW   1.56247954
## 2       BAU   Male            ALA   Direct      BW   2.34371931
## 3       BAU Female            DHA   Direct      BW   2.04671635
## 4       BAU   Male            DHA   Direct      BW   3.07007453
## 5       BAU Female           Fish   Direct      BW   0.44440477
## 6       BAU   Male           Fish   Direct      BW   0.66660715
## 7       BAU Female           MeHg   Direct      BW   0.02705835
## 8       BAU   Male           MeHg   Direct      BW   0.04058752
## 9       BAU Female         Omega3   Direct      BW   6.13267239
## 10      BAU   Male         Omega3   Direct      BW   9.19900859
## 11      BAU Female           PFAS   Direct      BW   4.16332442
## 12      BAU   Male           PFAS   Direct      BW   6.24498663
## 13      BAU Female            TEQ   Direct      BW   2.69609423
## 14      BAU   Male            TEQ   Direct      BW   4.04414134
## 15      BAU Female      Vitamin D   Direct      BW   0.03865415
## 16      BAU   Male      Vitamin D   Direct      BW   0.05798122
## 17      BAU Female           PFAS To child      BW   0.23415010
## 18      BAU   Male           PFAS To child      BW   0.23415010
## 19      BAU Female            TEQ To child      BW   0.35380662
## 20      BAU   Male            TEQ To child      BW   0.35380662
## 21      BAU Female            ALA   Direct   Log10   2.03891238
## 22      BAU   Male            ALA   Direct   Log10   2.21500364
## 23      BAU Female            DHA   Direct   Log10   2.15615570
## 24      BAU   Male            DHA   Direct   Log10   2.33224696
## 25      BAU Female           Fish   Direct   Log10   1.49287675
## 26      BAU   Male           Fish   Direct   Log10   1.66896801
## 27      BAU Female           MeHg   Direct   Log10   0.27739929
## 28      BAU   Male           MeHg   Direct   Log10   0.45349055
## 29      BAU Female         Omega3   Direct   Log10   2.63274781
## 30      BAU   Male         Omega3   Direct   Log10   2.80883906
## 31      BAU Female           PFAS   Direct   Log10   2.46453829
## 32      BAU   Male           PFAS   Direct   Log10   2.64062955
## 33      BAU Female            TEQ   Direct   Log10   2.27583311
## 34      BAU   Male            TEQ   Direct   Log10   2.45192437
## 35      BAU Female      Vitamin D   Direct   Log10   0.43229411
## 36      BAU   Male      Vitamin D   Direct   Log10   0.60838537
## 37      BAU Female           PFAS To child   Log10   1.21459239
## 38      BAU   Male           PFAS To child   Log10   1.21459239
## 39      BAU Female            TEQ To child   Log10   1.39386399
## 40      BAU   Male            TEQ To child   Log10   1.39386399
## 41      BAU Female            ALA   Direct    None 109.37356784
## 42      BAU   Male            ALA   Direct    None 164.06035175
## 43      BAU Female            DHA   Direct    None 143.27014453
## 44      BAU   Male            DHA   Direct    None 214.90521680
## 45      BAU Female           Fish   Direct    None  31.10833358
## 46      BAU   Male           Fish   Direct    None  46.66250037
## 47      BAU Female           MeHg   Direct    None   1.89408422
## 48      BAU   Male           MeHg   Direct    None   2.84112633
## 49      BAU Female         Omega3   Direct    None 429.28706762
## 50      BAU   Male         Omega3   Direct    None 643.93060143
## 51      BAU Female           PFAS   Direct    None 291.43270938
## 52      BAU   Male           PFAS   Direct    None 437.14906407
## 53      BAU Female            TEQ   Direct    None 188.72659592
## 54      BAU   Male            TEQ   Direct    None 283.08989388
## 55      BAU Female      Vitamin D   Direct    None   2.70579016
## 56      BAU   Male      Vitamin D   Direct    None   4.05868524
## 57      BAU Female           PFAS To child    None  16.39050716
## 58      BAU   Male           PFAS To child    None  16.39050716
## 59      BAU Female            TEQ To child    None  24.76646312
## 60      BAU   Male            TEQ To child    None  24.76646312
oprint(summary(ERF,"mean"))
##    Exposure_agent                                          Response
## 1            Fish                               All-cause mortality
## 2          Omega3                                     Breast cancer
## 3            Fish                                        Depression
## 4             DHA                         Loss in child's IQ points
## 5             TEQ                               Sperm concentration
## 6             TEQ                           Yes or no dental defect
## 7          Omega3                                    CHD2 mortality
## 8       Vitamin D                          Vitamin D recommendation
## 9            MeHg                         Loss in child's IQ points
## 10           PFAS                                 Immunosuppression
## 11           PFAS                                          PFAS TWI
## 12            TEQ                           Cancer morbidity yearly
## 13            TEQ      Dioxin recommendation tolerable daily intake
## 14            TEQ Dioxin recommendation tolerable daily intake 2018
## 15           Fish                               All-cause mortality
## 16         Omega3                                     Breast cancer
## 17           Fish                                        Depression
## 18            DHA                         Loss in child's IQ points
## 19            TEQ                               Sperm concentration
## 20            TEQ                           Yes or no dental defect
## 21         Omega3                                    CHD2 mortality
## 22      Vitamin D                          Vitamin D recommendation
## 23           MeHg                         Loss in child's IQ points
## 24           PFAS                                 Immunosuppression
## 25           PFAS                                          PFAS TWI
## 26            TEQ                           Cancer morbidity yearly
## 27            TEQ      Dioxin recommendation tolerable daily intake
## 28            TEQ Dioxin recommendation tolerable daily intake 2018
##      ER_function Scaling Observation          mean
## 1             RR    None         ERF   0.997871700
## 2             RR    None         ERF   0.999487200
## 3             RR    None         ERF   0.994690400
## 4            ERS    None         ERF  -0.001300000
## 5            ERS    None         ERF   0.000060000
## 6            ERS    None         ERF   0.001390971
## 7  Relative Hill    None         ERF  -0.170000000
## 8           Step    None         ERF 100.000000000
## 9            ERS      BW         ERF   9.800000000
## 10           ERS      BW         ERF   0.022700000
## 11           TWI      BW         ERF   4.400000000
## 12           CSF      BW         ERF   0.000010000
## 13           TDI      BW         ERF   2.000000000
## 14           TDI      BW         ERF   0.288900000
## 15            RR    None   Threshold   0.000000000
## 16            RR    None   Threshold   0.000000000
## 17            RR    None   Threshold   0.000000000
## 18           ERS    None   Threshold   0.000000000
## 19           ERS    None   Threshold   0.000000000
## 20           ERS    None   Threshold   0.000000000
## 21 Relative Hill    None   Threshold  47.000000000
## 22          Step    None   Threshold  10.000000000
## 23           ERS      BW   Threshold   0.000000000
## 24           ERS      BW   Threshold   0.000000000
## 25           TWI      BW   Threshold   0.000000000
## 26           CSF      BW   Threshold   0.000000000
## 27           TDI      BW   Threshold   0.000000000
## 28           TDI      BW   Threshold   0.000000000
oprint(summary(expo_dir,"mean"))
##                Kala Scenario Gender          Fish Exposure_agent         mean
## 1       Kaupallinen      BAU Female  Average fish            ALA   0.87611225
## 2        Muu tuonti      BAU Female  Average fish            ALA   7.66598220
## 3        Vapaa-ajan      BAU Female  Average fish            ALA   2.71047228
## 4       Kaupallinen      BAU   Male  Average fish            ALA   1.31416838
## 5        Muu tuonti      BAU   Male  Average fish            ALA  11.49897331
## 6        Vapaa-ajan      BAU   Male  Average fish            ALA   4.06570842
## 7           Silakka      BAU Female       Herring            ALA   1.10466327
## 8           Silakka      BAU   Male       Herring            ALA   1.65699491
## 9         Kirjolohi      BAU Female Rainbow trout            ALA  12.78735034
## 10  Tuontikirjolohi      BAU Female Rainbow trout            ALA   9.16108681
## 11        Kirjolohi      BAU   Male Rainbow trout            ALA  19.18102550
## 12  Tuontikirjolohi      BAU   Male Rainbow trout            ALA  13.74163021
## 13       Tuontilohi      BAU Female        Salmon            ALA  74.53937644
## 14       Tuontilohi      BAU   Male        Salmon            ALA 111.80906467
## 15       Kasvatettu      BAU Female     Whitefish            ALA   0.52852424
## 16       Kasvatettu      BAU   Male     Whitefish            ALA   0.79278636
## 17      Kaupallinen      BAU Female  Average fish            DHA   3.22510887
## 18       Muu tuonti      BAU Female  Average fish            DHA  28.21970261
## 19       Vapaa-ajan      BAU Female  Average fish            DHA   9.97768056
## 20      Kaupallinen      BAU   Male  Average fish            DHA   4.83766330
## 21       Muu tuonti      BAU   Male  Average fish            DHA  42.32955391
## 22       Vapaa-ajan      BAU   Male  Average fish            DHA  14.96652085
## 23          Silakka      BAU Female       Herring            DHA   3.72030275
## 24          Silakka      BAU   Male       Herring            DHA   5.58045413
## 25        Kirjolohi      BAU Female Rainbow trout            DHA  20.12479045
## 26  Tuontikirjolohi      BAU Female Rainbow trout            DHA  14.41776032
## 27        Kirjolohi      BAU   Male Rainbow trout            DHA  30.18718567
## 28  Tuontikirjolohi      BAU   Male Rainbow trout            DHA  21.62664048
## 29       Tuontilohi      BAU Female        Salmon            DHA  62.64678749
## 30       Tuontilohi      BAU   Male        Salmon            DHA  93.97018123
## 31       Kasvatettu      BAU Female     Whitefish            DHA   0.93801149
## 32       Kasvatettu      BAU   Male     Whitefish            DHA   1.40701723
## 33      Kaupallinen      BAU Female  Average fish           Fish   1.26972790
## 34       Muu tuonti      BAU Female  Average fish           Fish  11.11011914
## 35       Vapaa-ajan      BAU Female  Average fish           Fish   3.92822069
## 36      Kaupallinen      BAU   Male  Average fish           Fish   1.90459185
## 37       Muu tuonti      BAU   Male  Average fish           Fish  16.66517870
## 38       Vapaa-ajan      BAU   Male  Average fish           Fish   5.89233104
## 39          Silakka      BAU Female       Herring           Fish   0.63486395
## 40          Silakka      BAU   Male       Herring           Fish   0.95229593
## 41        Kirjolohi      BAU Female Rainbow trout           Fish   2.65849279
## 42  Tuontikirjolohi      BAU Female Rainbow trout           Fish   1.90459185
## 43        Kirjolohi      BAU   Male Rainbow trout           Fish   3.98773919
## 44  Tuontikirjolohi      BAU   Male Rainbow trout           Fish   2.85688778
## 45       Tuontilohi      BAU Female        Salmon           Fish   9.36424327
## 46       Tuontilohi      BAU   Male        Salmon           Fish  14.04636491
## 47       Kasvatettu      BAU Female     Whitefish           Fish   0.23807398
## 48       Kasvatettu      BAU   Male     Whitefish           Fish   0.35711097
## 49      Kaupallinen      BAU Female  Average fish           MeHg   0.14491907
## 50       Muu tuonti      BAU Female  Average fish           MeHg   1.26804182
## 51       Vapaa-ajan      BAU Female  Average fish           MeHg   0.44834336
## 52      Kaupallinen      BAU   Male  Average fish           MeHg   0.21737860
## 53       Muu tuonti      BAU   Male  Average fish           MeHg   1.90206273
## 54       Vapaa-ajan      BAU   Male  Average fish           MeHg   0.67251504
## 55          Silakka      BAU Female       Herring           MeHg   0.01702066
## 56          Silakka      BAU   Male       Herring           MeHg   0.02553100
## 57       Kasvatettu      BAU Female     Whitefish           MeHg   0.01575931
## 58       Kasvatettu      BAU   Male     Whitefish           MeHg   0.02363897
## 59      Kaupallinen      BAU Female  Average fish         Omega3   8.88809531
## 60       Muu tuonti      BAU Female  Average fish         Omega3  77.77083395
## 61       Vapaa-ajan      BAU Female  Average fish         Omega3  27.49754486
## 62      Kaupallinen      BAU   Male  Average fish         Omega3  13.33214296
## 63       Muu tuonti      BAU   Male  Average fish         Omega3 116.65625093
## 64       Vapaa-ajan      BAU   Male  Average fish         Omega3  41.24631729
## 65          Silakka      BAU Female       Herring         Omega3  15.23673482
## 66          Silakka      BAU   Male       Herring         Omega3  22.85510222
## 67        Kirjolohi      BAU Female Rainbow trout         Omega3  47.85287028
## 68  Tuontikirjolohi      BAU Female Rainbow trout         Omega3  34.28265333
## 69        Kirjolohi      BAU   Male Rainbow trout         Omega3  71.77930542
## 70  Tuontikirjolohi      BAU   Male Rainbow trout         Omega3  51.42398000
## 71       Tuontilohi      BAU Female        Salmon         Omega3 215.37759525
## 72       Tuontilohi      BAU   Male        Salmon         Omega3 323.06639288
## 73       Kasvatettu      BAU Female     Whitefish         Omega3   2.38073981
## 74       Kasvatettu      BAU   Male     Whitefish         Omega3   3.57110972
## 75      Kaupallinen      BAU Female  Average fish           PFAS  10.48795246
## 76       Muu tuonti      BAU Female  Average fish           PFAS  91.76958406
## 77       Vapaa-ajan      BAU Female  Average fish           PFAS  32.44710294
## 78      Kaupallinen      BAU   Male  Average fish           PFAS  15.73192870
## 79       Muu tuonti      BAU   Male  Average fish           PFAS 137.65437610
## 80       Vapaa-ajan      BAU   Male  Average fish           PFAS  48.67065441
## 81          Silakka      BAU Female       Herring           PFAS   1.05069984
## 82          Silakka      BAU   Male       Herring           PFAS   1.57604976
## 83        Kirjolohi      BAU Female Rainbow trout           PFAS  73.74659009
## 84  Tuontikirjolohi      BAU Female Rainbow trout           PFAS  52.83337797
## 85        Kirjolohi      BAU   Male Rainbow trout           PFAS 110.61988513
## 86  Tuontikirjolohi      BAU   Male Rainbow trout           PFAS  79.25006696
## 87       Tuontilohi      BAU Female        Salmon           PFAS  28.09272982
## 88       Tuontilohi      BAU   Male        Salmon           PFAS  42.13909472
## 89       Kasvatettu      BAU Female     Whitefish           PFAS   1.00467220
## 90       Kasvatettu      BAU   Male     Whitefish           PFAS   1.50700830
## 91       Tuontilohi      BAU Female        Salmon            TEQ 187.35774701
## 92       Tuontilohi      BAU   Male        Salmon            TEQ 281.03662052
## 93       Kasvatettu      BAU Female     Whitefish            TEQ   1.36884891
## 94       Kasvatettu      BAU   Male     Whitefish            TEQ   2.05327336
## 95      Kaupallinen      BAU Female  Average fish      Vitamin D   0.13332143
## 96       Muu tuonti      BAU Female  Average fish      Vitamin D   1.16656251
## 97       Vapaa-ajan      BAU Female  Average fish      Vitamin D   0.41246317
## 98      Kaupallinen      BAU   Male  Average fish      Vitamin D   0.19998214
## 99       Muu tuonti      BAU   Male  Average fish      Vitamin D   1.74984376
## 100      Vapaa-ajan      BAU   Male  Average fish      Vitamin D   0.61869476
## 101         Silakka      BAU Female       Herring      Vitamin D   0.09903878
## 102         Silakka      BAU   Male       Herring      Vitamin D   0.14855816
## 103       Kirjolohi      BAU Female Rainbow trout      Vitamin D   0.13558313
## 104 Tuontikirjolohi      BAU Female Rainbow trout      Vitamin D   0.09713418
## 105       Kirjolohi      BAU   Male Rainbow trout      Vitamin D   0.20337470
## 106 Tuontikirjolohi      BAU   Male Rainbow trout      Vitamin D   0.14570128
## 107      Tuontilohi      BAU Female        Salmon      Vitamin D   0.62740430
## 108      Tuontilohi      BAU   Male        Salmon      Vitamin D   0.94110645
## 109      Kasvatettu      BAU Female     Whitefish      Vitamin D   0.03428265
## 110      Kasvatettu      BAU   Male     Whitefish      Vitamin D   0.05142398
oprint(summary(expo_indir,"mean"))
##               Kala Scenario Gender          Fish Exposure_agent Exposure
## 1      Kaupallinen      BAU Female  Average fish           PFAS To child
## 2       Muu tuonti      BAU Female  Average fish           PFAS To child
## 3       Vapaa-ajan      BAU Female  Average fish           PFAS To child
## 4      Kaupallinen      BAU   Male  Average fish           PFAS To child
## 5       Muu tuonti      BAU   Male  Average fish           PFAS To child
## 6       Vapaa-ajan      BAU   Male  Average fish           PFAS To child
## 7          Silakka      BAU Female       Herring           PFAS To child
## 8          Silakka      BAU   Male       Herring           PFAS To child
## 9        Kirjolohi      BAU Female Rainbow trout           PFAS To child
## 10 Tuontikirjolohi      BAU Female Rainbow trout           PFAS To child
## 11       Kirjolohi      BAU   Male Rainbow trout           PFAS To child
## 12 Tuontikirjolohi      BAU   Male Rainbow trout           PFAS To child
## 13      Tuontilohi      BAU Female        Salmon           PFAS To child
## 14      Tuontilohi      BAU   Male        Salmon           PFAS To child
## 15      Kasvatettu      BAU Female     Whitefish           PFAS To child
## 16      Kasvatettu      BAU   Male     Whitefish           PFAS To child
## 17      Tuontilohi      BAU Female        Salmon            TEQ To child
## 18      Tuontilohi      BAU   Male        Salmon            TEQ To child
## 19      Kasvatettu      BAU Female     Whitefish            TEQ To child
## 20      Kasvatettu      BAU   Male     Whitefish            TEQ To child
##           mean
## 1   0.58985438
## 2   5.16122582
## 3   1.82486199
## 4   0.88478157
## 5   7.74183873
## 6   2.73729298
## 7   0.05909255
## 8   0.08863883
## 9   4.14759213
## 10  2.97140929
## 11  6.22138820
## 12  4.45711393
## 13  1.57996709
## 14  2.36995063
## 15  0.05650391
## 16  0.08475586
## 17 24.58683001
## 18 36.88024502
## 19  0.17963311
## 20  0.26944967
oprint(summary(exposure,"mean"))
##    Scenario Gender Exposure_agent Exposure       mean
## 1       BAU Female            ALA   Direct 109.373568
## 2       BAU   Male            ALA   Direct 164.060352
## 3       BAU Female            DHA   Direct 143.270145
## 4       BAU   Male            DHA   Direct 214.905217
## 5       BAU Female           Fish   Direct  31.108334
## 6       BAU   Male           Fish   Direct  46.662500
## 7       BAU Female           MeHg   Direct   1.894084
## 8       BAU   Male           MeHg   Direct   2.841126
## 9       BAU Female         Omega3   Direct 429.287068
## 10      BAU   Male         Omega3   Direct 643.930601
## 11      BAU Female           PFAS   Direct 291.432709
## 12      BAU   Male           PFAS   Direct 437.149064
## 13      BAU Female            TEQ   Direct 188.726596
## 14      BAU   Male            TEQ   Direct 283.089894
## 15      BAU Female      Vitamin D   Direct   2.705790
## 16      BAU   Male      Vitamin D   Direct   4.058685
## 17      BAU Female           PFAS To child  16.390507
## 18      BAU   Male           PFAS To child  16.390507
## 19      BAU Female            TEQ To child  24.766463
## 20      BAU   Male            TEQ To child  24.766463
#oprint(summary(fish_proportion,"mean"))
oprint(summary(incidence,"mean"))
##                                            Response       Age Adjust   mean
## 1                                 Immunosuppression     0 - 4    BAU 4.8100
## 2                         Loss in child's IQ points     0 - 4    BAU 1.1920
## 3                               Sperm concentration     0 - 4    BAU 0.0140
## 4                           Yes or no dental defect     0 - 4    BAU 0.0448
## 5                                 Immunosuppression     5 - 9    BAU 3.9500
## 6      Dioxin recommendation tolerable daily intake Undefined    BAU 0.1100
## 7 Dioxin recommendation tolerable daily intake 2018 Undefined    BAU 0.3200
## 8                                          PFAS TWI Undefined    BAU 1.0000
## 9                          Vitamin D recommendation Undefined    BAU 0.2200
oprint(summary(PAF,"mean"))
##     Exposure_agent                                          Response Scenario
## 1              DHA                         Loss in child's IQ points      BAU
## 2             MeHg                         Loss in child's IQ points      BAU
## 3              TEQ                           Cancer morbidity yearly      BAU
## 4              TEQ                               Sperm concentration      BAU
## 5              TEQ                           Yes or no dental defect      BAU
## 6             PFAS                                 Immunosuppression      BAU
## 7             Fish                               All-cause mortality      BAU
## 8           Omega3                                     Breast cancer      BAU
## 9             Fish                                        Depression      BAU
## 10          Omega3                                    CHD2 mortality      BAU
## 11             DHA                         Loss in child's IQ points      BAU
## 12            MeHg                         Loss in child's IQ points      BAU
## 13             TEQ                           Cancer morbidity yearly      BAU
## 14             TEQ                               Sperm concentration      BAU
## 15             TEQ                           Yes or no dental defect      BAU
## 16            PFAS                                 Immunosuppression      BAU
## 17            Fish                               All-cause mortality      BAU
## 18          Omega3                                     Breast cancer      BAU
## 19            Fish                                        Depression      BAU
## 20          Omega3                                    CHD2 mortality      BAU
## 21             TEQ                           Cancer morbidity yearly      BAU
## 22            Fish                               All-cause mortality      BAU
## 23          Omega3                                     Breast cancer      BAU
## 24            Fish                                        Depression      BAU
## 25          Omega3                                    CHD2 mortality      BAU
## 26             TEQ                           Cancer morbidity yearly      BAU
## 27            Fish                               All-cause mortality      BAU
## 28          Omega3                                     Breast cancer      BAU
## 29            Fish                                        Depression      BAU
## 30          Omega3                                    CHD2 mortality      BAU
## 31             TEQ                           Cancer morbidity yearly      BAU
## 32            Fish                               All-cause mortality      BAU
## 33          Omega3                                     Breast cancer      BAU
## 34            Fish                                        Depression      BAU
## 35          Omega3                                    CHD2 mortality      BAU
## 36             TEQ                           Cancer morbidity yearly      BAU
## 37            Fish                               All-cause mortality      BAU
## 38          Omega3                                     Breast cancer      BAU
## 39            Fish                                        Depression      BAU
## 40          Omega3                                    CHD2 mortality      BAU
## 41             TEQ                           Cancer morbidity yearly      BAU
## 42            Fish                               All-cause mortality      BAU
## 43          Omega3                                     Breast cancer      BAU
## 44            Fish                                        Depression      BAU
## 45          Omega3                                    CHD2 mortality      BAU
## 46             TEQ                           Cancer morbidity yearly      BAU
## 47            Fish                               All-cause mortality      BAU
## 48          Omega3                                     Breast cancer      BAU
## 49            Fish                                        Depression      BAU
## 50          Omega3                                    CHD2 mortality      BAU
## 51             TEQ                           Cancer morbidity yearly      BAU
## 52            Fish                               All-cause mortality      BAU
## 53          Omega3                                     Breast cancer      BAU
## 54            Fish                                        Depression      BAU
## 55          Omega3                                    CHD2 mortality      BAU
## 56             TEQ                           Cancer morbidity yearly      BAU
## 57            Fish                               All-cause mortality      BAU
## 58          Omega3                                     Breast cancer      BAU
## 59            Fish                                        Depression      BAU
## 60          Omega3                                    CHD2 mortality      BAU
## 61             TEQ                           Cancer morbidity yearly      BAU
## 62            Fish                               All-cause mortality      BAU
## 63          Omega3                                     Breast cancer      BAU
## 64            Fish                                        Depression      BAU
## 65          Omega3                                    CHD2 mortality      BAU
## 66             TEQ                           Cancer morbidity yearly      BAU
## 67            Fish                               All-cause mortality      BAU
## 68          Omega3                                     Breast cancer      BAU
## 69            Fish                                        Depression      BAU
## 70          Omega3                                    CHD2 mortality      BAU
## 71             TEQ                           Cancer morbidity yearly      BAU
## 72            Fish                               All-cause mortality      BAU
## 73          Omega3                                     Breast cancer      BAU
## 74            Fish                                        Depression      BAU
## 75          Omega3                                    CHD2 mortality      BAU
## 76             TEQ                           Cancer morbidity yearly      BAU
## 77            Fish                               All-cause mortality      BAU
## 78          Omega3                                     Breast cancer      BAU
## 79            Fish                                        Depression      BAU
## 80          Omega3                                    CHD2 mortality      BAU
## 81             TEQ                           Cancer morbidity yearly      BAU
## 82            Fish                               All-cause mortality      BAU
## 83          Omega3                                     Breast cancer      BAU
## 84            Fish                                        Depression      BAU
## 85          Omega3                                    CHD2 mortality      BAU
## 86             TEQ                           Cancer morbidity yearly      BAU
## 87            Fish                               All-cause mortality      BAU
## 88          Omega3                                     Breast cancer      BAU
## 89            Fish                                        Depression      BAU
## 90          Omega3                                    CHD2 mortality      BAU
## 91             TEQ                           Cancer morbidity yearly      BAU
## 92            Fish                               All-cause mortality      BAU
## 93          Omega3                                     Breast cancer      BAU
## 94            Fish                                        Depression      BAU
## 95          Omega3                                    CHD2 mortality      BAU
## 96             TEQ                           Cancer morbidity yearly      BAU
## 97            Fish                               All-cause mortality      BAU
## 98          Omega3                                     Breast cancer      BAU
## 99            Fish                                        Depression      BAU
## 100         Omega3                                    CHD2 mortality      BAU
## 101            TEQ                           Cancer morbidity yearly      BAU
## 102           PFAS                                 Immunosuppression      BAU
## 103           Fish                               All-cause mortality      BAU
## 104         Omega3                                     Breast cancer      BAU
## 105           Fish                                        Depression      BAU
## 106         Omega3                                    CHD2 mortality      BAU
## 107            TEQ                           Cancer morbidity yearly      BAU
## 108           PFAS                                 Immunosuppression      BAU
## 109           Fish                               All-cause mortality      BAU
## 110         Omega3                                     Breast cancer      BAU
## 111           Fish                                        Depression      BAU
## 112         Omega3                                    CHD2 mortality      BAU
## 113            TEQ                           Cancer morbidity yearly      BAU
## 114           Fish                               All-cause mortality      BAU
## 115         Omega3                                     Breast cancer      BAU
## 116           Fish                                        Depression      BAU
## 117         Omega3                                    CHD2 mortality      BAU
## 118            TEQ                           Cancer morbidity yearly      BAU
## 119           Fish                               All-cause mortality      BAU
## 120         Omega3                                     Breast cancer      BAU
## 121           Fish                                        Depression      BAU
## 122         Omega3                                    CHD2 mortality      BAU
## 123            TEQ                           Cancer morbidity yearly      BAU
## 124           Fish                               All-cause mortality      BAU
## 125         Omega3                                     Breast cancer      BAU
## 126           Fish                                        Depression      BAU
## 127         Omega3                                    CHD2 mortality      BAU
## 128            TEQ                           Cancer morbidity yearly      BAU
## 129           Fish                               All-cause mortality      BAU
## 130         Omega3                                     Breast cancer      BAU
## 131           Fish                                        Depression      BAU
## 132         Omega3                                    CHD2 mortality      BAU
## 133            TEQ                           Cancer morbidity yearly      BAU
## 134           Fish                               All-cause mortality      BAU
## 135         Omega3                                     Breast cancer      BAU
## 136           Fish                                        Depression      BAU
## 137         Omega3                                    CHD2 mortality      BAU
## 138            TEQ                           Cancer morbidity yearly      BAU
## 139           Fish                               All-cause mortality      BAU
## 140         Omega3                                     Breast cancer      BAU
## 141           Fish                                        Depression      BAU
## 142         Omega3                                    CHD2 mortality      BAU
## 143            TEQ                           Cancer morbidity yearly      BAU
## 144           Fish                               All-cause mortality      BAU
## 145         Omega3                                     Breast cancer      BAU
## 146           Fish                                        Depression      BAU
## 147         Omega3                                    CHD2 mortality      BAU
## 148            TEQ                           Cancer morbidity yearly      BAU
## 149           Fish                               All-cause mortality      BAU
## 150         Omega3                                     Breast cancer      BAU
## 151           Fish                                        Depression      BAU
## 152         Omega3                                    CHD2 mortality      BAU
## 153            TEQ                           Cancer morbidity yearly      BAU
## 154           Fish                               All-cause mortality      BAU
## 155         Omega3                                     Breast cancer      BAU
## 156           Fish                                        Depression      BAU
## 157         Omega3                                    CHD2 mortality      BAU
## 158            TEQ                           Cancer morbidity yearly      BAU
## 159           Fish                               All-cause mortality      BAU
## 160         Omega3                                     Breast cancer      BAU
## 161           Fish                                        Depression      BAU
## 162         Omega3                                    CHD2 mortality      BAU
## 163            TEQ                           Cancer morbidity yearly      BAU
## 164           Fish                               All-cause mortality      BAU
## 165         Omega3                                     Breast cancer      BAU
## 166           Fish                                        Depression      BAU
## 167         Omega3                                    CHD2 mortality      BAU
## 168            TEQ                           Cancer morbidity yearly      BAU
## 169           Fish                               All-cause mortality      BAU
## 170         Omega3                                     Breast cancer      BAU
## 171           Fish                                        Depression      BAU
## 172         Omega3                                    CHD2 mortality      BAU
## 173            TEQ                           Cancer morbidity yearly      BAU
## 174           Fish                               All-cause mortality      BAU
## 175         Omega3                                     Breast cancer      BAU
## 176           Fish                                        Depression      BAU
## 177         Omega3                                    CHD2 mortality      BAU
## 178            TEQ                           Cancer morbidity yearly      BAU
## 179           Fish                               All-cause mortality      BAU
## 180         Omega3                                     Breast cancer      BAU
## 181           Fish                                        Depression      BAU
## 182         Omega3                                    CHD2 mortality      BAU
## 183            TEQ                           Cancer morbidity yearly      BAU
## 184           Fish                               All-cause mortality      BAU
## 185         Omega3                                     Breast cancer      BAU
## 186           Fish                                        Depression      BAU
## 187         Omega3                                    CHD2 mortality      BAU
## 188            TEQ                           Cancer morbidity yearly      BAU
## 189           Fish                               All-cause mortality      BAU
## 190         Omega3                                     Breast cancer      BAU
## 191           Fish                                        Depression      BAU
## 192         Omega3                                    CHD2 mortality      BAU
## 193            TEQ                           Cancer morbidity yearly      BAU
## 194            TEQ      Dioxin recommendation tolerable daily intake      BAU
## 195            TEQ Dioxin recommendation tolerable daily intake 2018      BAU
## 196      Vitamin D                          Vitamin D recommendation      BAU
## 197           PFAS                                          PFAS TWI      BAU
## 198           Fish                               All-cause mortality      BAU
## 199         Omega3                                     Breast cancer      BAU
## 200           Fish                                        Depression      BAU
## 201         Omega3                                    CHD2 mortality      BAU
## 202            TEQ                           Cancer morbidity yearly      BAU
## 203            TEQ      Dioxin recommendation tolerable daily intake      BAU
## 204            TEQ Dioxin recommendation tolerable daily intake 2018      BAU
## 205      Vitamin D                          Vitamin D recommendation      BAU
## 206           PFAS                                          PFAS TWI      BAU
## 207           Fish                               All-cause mortality      BAU
## 208         Omega3                                     Breast cancer      BAU
## 209           Fish                                        Depression      BAU
## 210         Omega3                                    CHD2 mortality      BAU
##     Gender       Age Adjust         mean
## 1   Female     0 - 4    BAU -0.156250997
## 2   Female     0 - 4    BAU  0.222459556
## 3   Female     0 - 4    BAU  0.004642163
## 4   Female     0 - 4    BAU  0.914970253
## 5   Female     0 - 4    BAU  6.628630113
## 6   Female     0 - 4    BAU  0.020753154
## 7   Female     0 - 4    BAU -0.064129738
## 8   Female     0 - 4    BAU -0.197637574
## 9   Female     0 - 4    BAU -0.152625783
## 10  Female     0 - 4    BAU -0.153224403
## 11    Male     0 - 4    BAU -0.234376495
## 12    Male     0 - 4    BAU  0.333689334
## 13    Male     0 - 4    BAU  0.006693985
## 14    Male     0 - 4    BAU  1.319384387
## 15    Male     0 - 4    BAU  9.558464934
## 16    Male     0 - 4    BAU  0.030577215
## 17    Male     0 - 4    BAU -0.094635480
## 18    Male     0 - 4    BAU -0.281286382
## 19    Male     0 - 4    BAU -0.219967194
## 20    Male     0 - 4    BAU -0.158435886
## 21  Female   10 - 14    BAU  0.004642163
## 22  Female   10 - 14    BAU -0.064129738
## 23  Female   10 - 14    BAU -0.197637574
## 24  Female   10 - 14    BAU -0.152625783
## 25  Female   10 - 14    BAU -0.153224403
## 26    Male   10 - 14    BAU  0.006693985
## 27    Male   10 - 14    BAU -0.094635480
## 28    Male   10 - 14    BAU -0.281286382
## 29    Male   10 - 14    BAU -0.219967194
## 30    Male   10 - 14    BAU -0.158435886
## 31  Female   15 - 19    BAU  0.004642163
## 32  Female   15 - 19    BAU -0.064129738
## 33  Female   15 - 19    BAU -0.197637574
## 34  Female   15 - 19    BAU -0.152625783
## 35  Female   15 - 19    BAU -0.153224403
## 36    Male   15 - 19    BAU  0.006693985
## 37    Male   15 - 19    BAU -0.094635480
## 38    Male   15 - 19    BAU -0.281286382
## 39    Male   15 - 19    BAU -0.219967194
## 40    Male   15 - 19    BAU -0.158435886
## 41  Female   20 - 24    BAU  0.004642163
## 42  Female   20 - 24    BAU -0.064129738
## 43  Female   20 - 24    BAU -0.197637574
## 44  Female   20 - 24    BAU -0.152625783
## 45  Female   20 - 24    BAU -0.153224403
## 46    Male   20 - 24    BAU  0.006693985
## 47    Male   20 - 24    BAU -0.094635480
## 48    Male   20 - 24    BAU -0.281286382
## 49    Male   20 - 24    BAU -0.219967194
## 50    Male   20 - 24    BAU -0.158435886
## 51  Female   25 - 29    BAU  0.004642163
## 52  Female   25 - 29    BAU -0.064129738
## 53  Female   25 - 29    BAU -0.197637574
## 54  Female   25 - 29    BAU -0.152625783
## 55  Female   25 - 29    BAU -0.153224403
## 56    Male   25 - 29    BAU  0.006693985
## 57    Male   25 - 29    BAU -0.094635480
## 58    Male   25 - 29    BAU -0.281286382
## 59    Male   25 - 29    BAU -0.219967194
## 60    Male   25 - 29    BAU -0.158435886
## 61  Female   30 - 34    BAU  0.004642163
## 62  Female   30 - 34    BAU -0.064129738
## 63  Female   30 - 34    BAU -0.197637574
## 64  Female   30 - 34    BAU -0.152625783
## 65  Female   30 - 34    BAU -0.153224403
## 66    Male   30 - 34    BAU  0.006693985
## 67    Male   30 - 34    BAU -0.094635480
## 68    Male   30 - 34    BAU -0.281286382
## 69    Male   30 - 34    BAU -0.219967194
## 70    Male   30 - 34    BAU -0.158435886
## 71  Female   35 - 39    BAU  0.004642163
## 72  Female   35 - 39    BAU -0.064129738
## 73  Female   35 - 39    BAU -0.197637574
## 74  Female   35 - 39    BAU -0.152625783
## 75  Female   35 - 39    BAU -0.153224403
## 76    Male   35 - 39    BAU  0.006693985
## 77    Male   35 - 39    BAU -0.094635480
## 78    Male   35 - 39    BAU -0.281286382
## 79    Male   35 - 39    BAU -0.219967194
## 80    Male   35 - 39    BAU -0.158435886
## 81  Female   40 - 44    BAU  0.004642163
## 82  Female   40 - 44    BAU -0.064129738
## 83  Female   40 - 44    BAU -0.197637574
## 84  Female   40 - 44    BAU -0.152625783
## 85  Female   40 - 44    BAU -0.153224403
## 86    Male   40 - 44    BAU  0.006693985
## 87    Male   40 - 44    BAU -0.094635480
## 88    Male   40 - 44    BAU -0.281286382
## 89    Male   40 - 44    BAU -0.219967194
## 90    Male   40 - 44    BAU -0.158435886
## 91  Female   45 - 49    BAU  0.004642163
## 92  Female   45 - 49    BAU -0.064129738
## 93  Female   45 - 49    BAU -0.197637574
## 94  Female   45 - 49    BAU -0.152625783
## 95  Female   45 - 49    BAU -0.153224403
## 96    Male   45 - 49    BAU  0.006693985
## 97    Male   45 - 49    BAU -0.094635480
## 98    Male   45 - 49    BAU -0.281286382
## 99    Male   45 - 49    BAU -0.219967194
## 100   Male   45 - 49    BAU -0.158435886
## 101 Female     5 - 9    BAU  0.004642163
## 102 Female     5 - 9    BAU  0.025271562
## 103 Female     5 - 9    BAU -0.064129738
## 104 Female     5 - 9    BAU -0.197637574
## 105 Female     5 - 9    BAU -0.152625783
## 106 Female     5 - 9    BAU -0.153224403
## 107   Male     5 - 9    BAU  0.006693985
## 108   Male     5 - 9    BAU  0.037234533
## 109   Male     5 - 9    BAU -0.094635480
## 110   Male     5 - 9    BAU -0.281286382
## 111   Male     5 - 9    BAU -0.219967194
## 112   Male     5 - 9    BAU -0.158435886
## 113 Female   50 - 54    BAU  0.004642163
## 114 Female   50 - 54    BAU -0.064129738
## 115 Female   50 - 54    BAU -0.197637574
## 116 Female   50 - 54    BAU -0.152625783
## 117 Female   50 - 54    BAU -0.153224403
## 118   Male   50 - 54    BAU  0.006693985
## 119   Male   50 - 54    BAU -0.094635480
## 120   Male   50 - 54    BAU -0.281286382
## 121   Male   50 - 54    BAU -0.219967194
## 122   Male   50 - 54    BAU -0.158435886
## 123 Female   55 - 59    BAU  0.004642163
## 124 Female   55 - 59    BAU -0.064129738
## 125 Female   55 - 59    BAU -0.197637574
## 126 Female   55 - 59    BAU -0.152625783
## 127 Female   55 - 59    BAU -0.153224403
## 128   Male   55 - 59    BAU  0.006693985
## 129   Male   55 - 59    BAU -0.094635480
## 130   Male   55 - 59    BAU -0.281286382
## 131   Male   55 - 59    BAU -0.219967194
## 132   Male   55 - 59    BAU -0.158435886
## 133 Female   60 - 64    BAU  0.004642163
## 134 Female   60 - 64    BAU -0.064129738
## 135 Female   60 - 64    BAU -0.197637574
## 136 Female   60 - 64    BAU -0.152625783
## 137 Female   60 - 64    BAU -0.153224403
## 138   Male   60 - 64    BAU  0.006693985
## 139   Male   60 - 64    BAU -0.094635480
## 140   Male   60 - 64    BAU -0.281286382
## 141   Male   60 - 64    BAU -0.219967194
## 142   Male   60 - 64    BAU -0.158435886
## 143 Female   65 - 69    BAU  0.004642163
## 144 Female   65 - 69    BAU -0.064129738
## 145 Female   65 - 69    BAU -0.197637574
## 146 Female   65 - 69    BAU -0.152625783
## 147 Female   65 - 69    BAU -0.153224403
## 148   Male   65 - 69    BAU  0.006693985
## 149   Male   65 - 69    BAU -0.094635480
## 150   Male   65 - 69    BAU -0.281286382
## 151   Male   65 - 69    BAU -0.219967194
## 152   Male   65 - 69    BAU -0.158435886
## 153 Female   70 - 74    BAU  0.004642163
## 154 Female   70 - 74    BAU -0.064129738
## 155 Female   70 - 74    BAU -0.197637574
## 156 Female   70 - 74    BAU -0.152625783
## 157 Female   70 - 74    BAU -0.153224403
## 158   Male   70 - 74    BAU  0.006693985
## 159   Male   70 - 74    BAU -0.094635480
## 160   Male   70 - 74    BAU -0.281286382
## 161   Male   70 - 74    BAU -0.219967194
## 162   Male   70 - 74    BAU -0.158435886
## 163 Female   75 - 79    BAU  0.004642163
## 164 Female   75 - 79    BAU -0.064129738
## 165 Female   75 - 79    BAU -0.197637574
## 166 Female   75 - 79    BAU -0.152625783
## 167 Female   75 - 79    BAU -0.153224403
## 168   Male   75 - 79    BAU  0.006693985
## 169   Male   75 - 79    BAU -0.094635480
## 170   Male   75 - 79    BAU -0.281286382
## 171   Male   75 - 79    BAU -0.219967194
## 172   Male   75 - 79    BAU -0.158435886
## 173 Female   80 - 84    BAU  0.004642163
## 174 Female   80 - 84    BAU -0.064129738
## 175 Female   80 - 84    BAU -0.197637574
## 176 Female   80 - 84    BAU -0.152625783
## 177 Female   80 - 84    BAU -0.153224403
## 178   Male   80 - 84    BAU  0.006693985
## 179   Male   80 - 84    BAU -0.094635480
## 180   Male   80 - 84    BAU -0.281286382
## 181   Male   80 - 84    BAU -0.219967194
## 182   Male   80 - 84    BAU -0.158435886
## 183 Female       85+    BAU  0.004642163
## 184 Female       85+    BAU -0.064129738
## 185 Female       85+    BAU -0.197637574
## 186 Female       85+    BAU -0.152625783
## 187 Female       85+    BAU -0.153224403
## 188   Male       85+    BAU  0.006693985
## 189   Male       85+    BAU -0.094635480
## 190   Male       85+    BAU -0.281286382
## 191   Male       85+    BAU -0.219967194
## 192   Male       85+    BAU -0.158435886
## 193 Female Undefined    BAU  0.004642163
## 194 Female Undefined    BAU -7.090909091
## 195 Female Undefined    BAU  2.000000000
## 196 Female Undefined    BAU  1.000000000
## 197 Female Undefined    BAU  0.000000000
## 198 Female Undefined    BAU -0.064129738
## 199 Female Undefined    BAU -0.197637574
## 200 Female Undefined    BAU -0.152625783
## 201 Female Undefined    BAU -0.153224403
## 202   Male Undefined    BAU  0.006693985
## 203   Male Undefined    BAU -7.090909091
## 204   Male Undefined    BAU  2.000000000
## 205   Male Undefined    BAU  1.000000000
## 206   Male Undefined    BAU  1.000000000
## 207   Male Undefined    BAU -0.094635480
## 208   Male Undefined    BAU -0.281286382
## 209   Male Undefined    BAU -0.219967194
## 210   Male Undefined    BAU -0.158435886
oprint(summary(population,"mean"))
##    Gender       Age    mean
## 1  Female     0 - 4  125040
## 2    Male     0 - 4  130884
## 3  Female     5 - 9  149633
## 4    Male     5 - 9  156654
## 5  Female   10 - 14  151113
## 6    Male   10 - 14  157712
## 7  Female   15 - 19  144441
## 8    Male   15 - 19  152230
## 9  Female   20 - 24  152265
## 10   Male   20 - 24  161679
## 11 Female   25 - 29  172593
## 12   Male   25 - 29  183092
## 13 Female   30 - 34  169653
## 14   Male   30 - 34  181115
## 15 Female   35 - 39  174660
## 16   Male   35 - 39  186122
## 17 Female   40 - 44  168547
## 18   Male   40 - 44  177928
## 19 Female   45 - 49  154391
## 20   Male   45 - 49  159982
## 21 Female   50 - 54  176612
## 22   Male   50 - 54  179182
## 23 Female   55 - 59  185152
## 24   Male   55 - 59  183719
## 25 Female   60 - 64  183336
## 26   Male   60 - 64  176283
## 27 Female   65 - 69  185685
## 28   Male   65 - 69  171275
## 29 Female   70 - 74  186034
## 30   Male   70 - 74  163697
## 31 Female   75 - 79  118190
## 32   Male   75 - 79   93987
## 33 Female   80 - 84   96256
## 34   Male   80 - 84   65140
## 35 Female       85+  103429
## 36   Male       85+   47581
## 37 Female Undefined 2797030
## 38   Male Undefined 2728262
oprint(summary(RR,"mean"))
##     Exposure_agent            Response   ER_function Scaling Scenario Gender
## 1             Fish All-cause mortality            RR    None      BAU Female
## 2           Omega3       Breast cancer            RR    None      BAU Female
## 3             Fish          Depression            RR    None      BAU Female
## 4           Omega3      CHD2 mortality Relative Hill    None      BAU Female
## 5             Fish All-cause mortality            RR    None      BAU   Male
## 6           Omega3       Breast cancer            RR    None      BAU   Male
## 7             Fish          Depression            RR    None      BAU   Male
## 8           Omega3      CHD2 mortality Relative Hill    None      BAU   Male
## 9             Fish All-cause mortality            RR    None      BAU Female
## 10          Omega3       Breast cancer            RR    None      BAU Female
## 11            Fish          Depression            RR    None      BAU Female
## 12          Omega3      CHD2 mortality Relative Hill    None      BAU Female
## 13            Fish All-cause mortality            RR    None      BAU   Male
## 14          Omega3       Breast cancer            RR    None      BAU   Male
## 15            Fish          Depression            RR    None      BAU   Male
## 16          Omega3      CHD2 mortality Relative Hill    None      BAU   Male
## 17            Fish All-cause mortality            RR    None      BAU Female
## 18          Omega3       Breast cancer            RR    None      BAU Female
## 19            Fish          Depression            RR    None      BAU Female
## 20          Omega3      CHD2 mortality Relative Hill    None      BAU Female
## 21            Fish All-cause mortality            RR    None      BAU   Male
## 22          Omega3       Breast cancer            RR    None      BAU   Male
## 23            Fish          Depression            RR    None      BAU   Male
## 24          Omega3      CHD2 mortality Relative Hill    None      BAU   Male
## 25            Fish All-cause mortality            RR    None      BAU Female
## 26          Omega3       Breast cancer            RR    None      BAU Female
## 27            Fish          Depression            RR    None      BAU Female
## 28          Omega3      CHD2 mortality Relative Hill    None      BAU Female
## 29            Fish All-cause mortality            RR    None      BAU   Male
## 30          Omega3       Breast cancer            RR    None      BAU   Male
## 31            Fish          Depression            RR    None      BAU   Male
## 32          Omega3      CHD2 mortality Relative Hill    None      BAU   Male
## 33            Fish All-cause mortality            RR    None      BAU Female
## 34          Omega3       Breast cancer            RR    None      BAU Female
## 35            Fish          Depression            RR    None      BAU Female
## 36          Omega3      CHD2 mortality Relative Hill    None      BAU Female
## 37            Fish All-cause mortality            RR    None      BAU   Male
## 38          Omega3       Breast cancer            RR    None      BAU   Male
## 39            Fish          Depression            RR    None      BAU   Male
## 40          Omega3      CHD2 mortality Relative Hill    None      BAU   Male
## 41            Fish All-cause mortality            RR    None      BAU Female
## 42          Omega3       Breast cancer            RR    None      BAU Female
## 43            Fish          Depression            RR    None      BAU Female
## 44          Omega3      CHD2 mortality Relative Hill    None      BAU Female
## 45            Fish All-cause mortality            RR    None      BAU   Male
## 46          Omega3       Breast cancer            RR    None      BAU   Male
## 47            Fish          Depression            RR    None      BAU   Male
## 48          Omega3      CHD2 mortality Relative Hill    None      BAU   Male
## 49            Fish All-cause mortality            RR    None      BAU Female
## 50          Omega3       Breast cancer            RR    None      BAU Female
## 51            Fish          Depression            RR    None      BAU Female
## 52          Omega3      CHD2 mortality Relative Hill    None      BAU Female
## 53            Fish All-cause mortality            RR    None      BAU   Male
## 54          Omega3       Breast cancer            RR    None      BAU   Male
## 55            Fish          Depression            RR    None      BAU   Male
## 56          Omega3      CHD2 mortality Relative Hill    None      BAU   Male
## 57            Fish All-cause mortality            RR    None      BAU Female
## 58          Omega3       Breast cancer            RR    None      BAU Female
## 59            Fish          Depression            RR    None      BAU Female
## 60          Omega3      CHD2 mortality Relative Hill    None      BAU Female
## 61            Fish All-cause mortality            RR    None      BAU   Male
## 62          Omega3       Breast cancer            RR    None      BAU   Male
## 63            Fish          Depression            RR    None      BAU   Male
## 64          Omega3      CHD2 mortality Relative Hill    None      BAU   Male
## 65            Fish All-cause mortality            RR    None      BAU Female
## 66          Omega3       Breast cancer            RR    None      BAU Female
## 67            Fish          Depression            RR    None      BAU Female
## 68          Omega3      CHD2 mortality Relative Hill    None      BAU Female
## 69            Fish All-cause mortality            RR    None      BAU   Male
## 70          Omega3       Breast cancer            RR    None      BAU   Male
## 71            Fish          Depression            RR    None      BAU   Male
## 72          Omega3      CHD2 mortality Relative Hill    None      BAU   Male
## 73            Fish All-cause mortality            RR    None      BAU Female
## 74          Omega3       Breast cancer            RR    None      BAU Female
## 75            Fish          Depression            RR    None      BAU Female
## 76          Omega3      CHD2 mortality Relative Hill    None      BAU Female
## 77            Fish All-cause mortality            RR    None      BAU   Male
## 78          Omega3       Breast cancer            RR    None      BAU   Male
## 79            Fish          Depression            RR    None      BAU   Male
## 80          Omega3      CHD2 mortality Relative Hill    None      BAU   Male
## 81            Fish All-cause mortality            RR    None      BAU Female
## 82          Omega3       Breast cancer            RR    None      BAU Female
## 83            Fish          Depression            RR    None      BAU Female
## 84          Omega3      CHD2 mortality Relative Hill    None      BAU Female
## 85            Fish All-cause mortality            RR    None      BAU   Male
## 86          Omega3       Breast cancer            RR    None      BAU   Male
## 87            Fish          Depression            RR    None      BAU   Male
## 88          Omega3      CHD2 mortality Relative Hill    None      BAU   Male
## 89            Fish All-cause mortality            RR    None      BAU Female
## 90          Omega3       Breast cancer            RR    None      BAU Female
## 91            Fish          Depression            RR    None      BAU Female
## 92          Omega3      CHD2 mortality Relative Hill    None      BAU Female
## 93            Fish All-cause mortality            RR    None      BAU   Male
## 94          Omega3       Breast cancer            RR    None      BAU   Male
## 95            Fish          Depression            RR    None      BAU   Male
## 96          Omega3      CHD2 mortality Relative Hill    None      BAU   Male
## 97            Fish All-cause mortality            RR    None      BAU Female
## 98          Omega3       Breast cancer            RR    None      BAU Female
## 99            Fish          Depression            RR    None      BAU Female
## 100         Omega3      CHD2 mortality Relative Hill    None      BAU Female
## 101           Fish All-cause mortality            RR    None      BAU   Male
## 102         Omega3       Breast cancer            RR    None      BAU   Male
## 103           Fish          Depression            RR    None      BAU   Male
## 104         Omega3      CHD2 mortality Relative Hill    None      BAU   Male
## 105           Fish All-cause mortality            RR    None      BAU Female
## 106         Omega3       Breast cancer            RR    None      BAU Female
## 107           Fish          Depression            RR    None      BAU Female
## 108         Omega3      CHD2 mortality Relative Hill    None      BAU Female
## 109           Fish All-cause mortality            RR    None      BAU   Male
## 110         Omega3       Breast cancer            RR    None      BAU   Male
## 111           Fish          Depression            RR    None      BAU   Male
## 112         Omega3      CHD2 mortality Relative Hill    None      BAU   Male
## 113           Fish All-cause mortality            RR    None      BAU Female
## 114         Omega3       Breast cancer            RR    None      BAU Female
## 115           Fish          Depression            RR    None      BAU Female
## 116         Omega3      CHD2 mortality Relative Hill    None      BAU Female
## 117           Fish All-cause mortality            RR    None      BAU   Male
## 118         Omega3       Breast cancer            RR    None      BAU   Male
## 119           Fish          Depression            RR    None      BAU   Male
## 120         Omega3      CHD2 mortality Relative Hill    None      BAU   Male
## 121           Fish All-cause mortality            RR    None      BAU Female
## 122         Omega3       Breast cancer            RR    None      BAU Female
## 123           Fish          Depression            RR    None      BAU Female
## 124         Omega3      CHD2 mortality Relative Hill    None      BAU Female
## 125           Fish All-cause mortality            RR    None      BAU   Male
## 126         Omega3       Breast cancer            RR    None      BAU   Male
## 127           Fish          Depression            RR    None      BAU   Male
## 128         Omega3      CHD2 mortality Relative Hill    None      BAU   Male
## 129           Fish All-cause mortality            RR    None      BAU Female
## 130         Omega3       Breast cancer            RR    None      BAU Female
## 131           Fish          Depression            RR    None      BAU Female
## 132         Omega3      CHD2 mortality Relative Hill    None      BAU Female
## 133           Fish All-cause mortality            RR    None      BAU   Male
## 134         Omega3       Breast cancer            RR    None      BAU   Male
## 135           Fish          Depression            RR    None      BAU   Male
## 136         Omega3      CHD2 mortality Relative Hill    None      BAU   Male
## 137           Fish All-cause mortality            RR    None      BAU Female
## 138         Omega3       Breast cancer            RR    None      BAU Female
## 139           Fish          Depression            RR    None      BAU Female
## 140         Omega3      CHD2 mortality Relative Hill    None      BAU Female
## 141           Fish All-cause mortality            RR    None      BAU   Male
## 142         Omega3       Breast cancer            RR    None      BAU   Male
## 143           Fish          Depression            RR    None      BAU   Male
## 144         Omega3      CHD2 mortality Relative Hill    None      BAU   Male
## 145           Fish All-cause mortality            RR    None      BAU Female
## 146         Omega3       Breast cancer            RR    None      BAU Female
## 147           Fish          Depression            RR    None      BAU Female
## 148         Omega3      CHD2 mortality Relative Hill    None      BAU Female
## 149           Fish All-cause mortality            RR    None      BAU   Male
## 150         Omega3       Breast cancer            RR    None      BAU   Male
## 151           Fish          Depression            RR    None      BAU   Male
## 152         Omega3      CHD2 mortality Relative Hill    None      BAU   Male
##     Exposure       Age      mean
## 1     Direct     0 - 4 0.9358703
## 2     Direct     0 - 4 0.8023624
## 3     Direct     0 - 4 0.8473742
## 4     Direct     0 - 4 0.8467756
## 5     Direct     0 - 4 0.9053645
## 6     Direct     0 - 4 0.7187136
## 7     Direct     0 - 4 0.7800328
## 8     Direct     0 - 4 0.8415641
## 9     Direct     5 - 9 0.9358703
## 10    Direct     5 - 9 0.8023624
## 11    Direct     5 - 9 0.8473742
## 12    Direct     5 - 9 0.8467756
## 13    Direct     5 - 9 0.9053645
## 14    Direct     5 - 9 0.7187136
## 15    Direct     5 - 9 0.7800328
## 16    Direct     5 - 9 0.8415641
## 17    Direct   10 - 14 0.9358703
## 18    Direct   10 - 14 0.8023624
## 19    Direct   10 - 14 0.8473742
## 20    Direct   10 - 14 0.8467756
## 21    Direct   10 - 14 0.9053645
## 22    Direct   10 - 14 0.7187136
## 23    Direct   10 - 14 0.7800328
## 24    Direct   10 - 14 0.8415641
## 25    Direct   15 - 19 0.9358703
## 26    Direct   15 - 19 0.8023624
## 27    Direct   15 - 19 0.8473742
## 28    Direct   15 - 19 0.8467756
## 29    Direct   15 - 19 0.9053645
## 30    Direct   15 - 19 0.7187136
## 31    Direct   15 - 19 0.7800328
## 32    Direct   15 - 19 0.8415641
## 33    Direct   20 - 24 0.9358703
## 34    Direct   20 - 24 0.8023624
## 35    Direct   20 - 24 0.8473742
## 36    Direct   20 - 24 0.8467756
## 37    Direct   20 - 24 0.9053645
## 38    Direct   20 - 24 0.7187136
## 39    Direct   20 - 24 0.7800328
## 40    Direct   20 - 24 0.8415641
## 41    Direct   25 - 29 0.9358703
## 42    Direct   25 - 29 0.8023624
## 43    Direct   25 - 29 0.8473742
## 44    Direct   25 - 29 0.8467756
## 45    Direct   25 - 29 0.9053645
## 46    Direct   25 - 29 0.7187136
## 47    Direct   25 - 29 0.7800328
## 48    Direct   25 - 29 0.8415641
## 49    Direct   30 - 34 0.9358703
## 50    Direct   30 - 34 0.8023624
## 51    Direct   30 - 34 0.8473742
## 52    Direct   30 - 34 0.8467756
## 53    Direct   30 - 34 0.9053645
## 54    Direct   30 - 34 0.7187136
## 55    Direct   30 - 34 0.7800328
## 56    Direct   30 - 34 0.8415641
## 57    Direct   35 - 39 0.9358703
## 58    Direct   35 - 39 0.8023624
## 59    Direct   35 - 39 0.8473742
## 60    Direct   35 - 39 0.8467756
## 61    Direct   35 - 39 0.9053645
## 62    Direct   35 - 39 0.7187136
## 63    Direct   35 - 39 0.7800328
## 64    Direct   35 - 39 0.8415641
## 65    Direct   40 - 44 0.9358703
## 66    Direct   40 - 44 0.8023624
## 67    Direct   40 - 44 0.8473742
## 68    Direct   40 - 44 0.8467756
## 69    Direct   40 - 44 0.9053645
## 70    Direct   40 - 44 0.7187136
## 71    Direct   40 - 44 0.7800328
## 72    Direct   40 - 44 0.8415641
## 73    Direct   45 - 49 0.9358703
## 74    Direct   45 - 49 0.8023624
## 75    Direct   45 - 49 0.8473742
## 76    Direct   45 - 49 0.8467756
## 77    Direct   45 - 49 0.9053645
## 78    Direct   45 - 49 0.7187136
## 79    Direct   45 - 49 0.7800328
## 80    Direct   45 - 49 0.8415641
## 81    Direct   50 - 54 0.9358703
## 82    Direct   50 - 54 0.8023624
## 83    Direct   50 - 54 0.8473742
## 84    Direct   50 - 54 0.8467756
## 85    Direct   50 - 54 0.9053645
## 86    Direct   50 - 54 0.7187136
## 87    Direct   50 - 54 0.7800328
## 88    Direct   50 - 54 0.8415641
## 89    Direct   55 - 59 0.9358703
## 90    Direct   55 - 59 0.8023624
## 91    Direct   55 - 59 0.8473742
## 92    Direct   55 - 59 0.8467756
## 93    Direct   55 - 59 0.9053645
## 94    Direct   55 - 59 0.7187136
## 95    Direct   55 - 59 0.7800328
## 96    Direct   55 - 59 0.8415641
## 97    Direct   60 - 64 0.9358703
## 98    Direct   60 - 64 0.8023624
## 99    Direct   60 - 64 0.8473742
## 100   Direct   60 - 64 0.8467756
## 101   Direct   60 - 64 0.9053645
## 102   Direct   60 - 64 0.7187136
## 103   Direct   60 - 64 0.7800328
## 104   Direct   60 - 64 0.8415641
## 105   Direct   65 - 69 0.9358703
## 106   Direct   65 - 69 0.8023624
## 107   Direct   65 - 69 0.8473742
## 108   Direct   65 - 69 0.8467756
## 109   Direct   65 - 69 0.9053645
## 110   Direct   65 - 69 0.7187136
## 111   Direct   65 - 69 0.7800328
## 112   Direct   65 - 69 0.8415641
## 113   Direct   70 - 74 0.9358703
## 114   Direct   70 - 74 0.8023624
## 115   Direct   70 - 74 0.8473742
## 116   Direct   70 - 74 0.8467756
## 117   Direct   70 - 74 0.9053645
## 118   Direct   70 - 74 0.7187136
## 119   Direct   70 - 74 0.7800328
## 120   Direct   70 - 74 0.8415641
## 121   Direct   75 - 79 0.9358703
## 122   Direct   75 - 79 0.8023624
## 123   Direct   75 - 79 0.8473742
## 124   Direct   75 - 79 0.8467756
## 125   Direct   75 - 79 0.9053645
## 126   Direct   75 - 79 0.7187136
## 127   Direct   75 - 79 0.7800328
## 128   Direct   75 - 79 0.8415641
## 129   Direct   80 - 84 0.9358703
## 130   Direct   80 - 84 0.8023624
## 131   Direct   80 - 84 0.8473742
## 132   Direct   80 - 84 0.8467756
## 133   Direct   80 - 84 0.9053645
## 134   Direct   80 - 84 0.7187136
## 135   Direct   80 - 84 0.7800328
## 136   Direct   80 - 84 0.8415641
## 137   Direct       85+ 0.9358703
## 138   Direct       85+ 0.8023624
## 139   Direct       85+ 0.8473742
## 140   Direct       85+ 0.8467756
## 141   Direct       85+ 0.9053645
## 142   Direct       85+ 0.7187136
## 143   Direct       85+ 0.7800328
## 144   Direct       85+ 0.8415641
## 145   Direct Undefined 0.9358703
## 146   Direct Undefined 0.8023624
## 147   Direct Undefined 0.8473742
## 148   Direct Undefined 0.8467756
## 149   Direct Undefined 0.9053645
## 150   Direct Undefined 0.7187136
## 151   Direct Undefined 0.7800328
## 152   Direct Undefined 0.8415641
###################
# Graphs
trim <- function(ova) return(oapply(ova, NULL, mean, "Iter")@output)

ggplot(amount@output, aes(x=Gender, weight=amountResult, fill=Kala))+geom_bar()+
  labs(
    title="Kalansyönti Suomessa ikäryhmittäin",
    y="Syönti (g/d)"
  )

ggsave("Kalansyönti Suomessa ikäryhmittäin.svg")
## Saving 7 x 5 in image
plot_ly(trim(total_amount), x=~Kala, y=~total_amountResult, color=~Kala, type="bar") %>%
  layout(yaxis=list(title="Kalan kokonaiskulutus Suomessa (milj kg /a)"), barmode="stack")
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
plot_ly(trim(conc_vit), x=~Nutrient, y=~conc_vitResult, color=~Kala, type="scatter", mode="markers") %>%
  layout(yaxis=list(title="Concentrations of nutrients (mg or ug /g)"))
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
ggplot(conc_pfas@output, aes(x=Fish, y=conc_pfasResult))+geom_point()+ # Inputed data for missing species.
  coord_flip()

ggplot(conc_pfas@output, aes(x=conc_pfasResult, color=Compound, linetype=Area))+stat_ecdf()+
  scale_x_log10()+
  stat_ecdf(data=conc_eukalat@output, aes(x=conc_eukalatResult))+
  scale_linetype_manual(values=c("dotted","solid","twodash"))+
  labs(
    title="PFAS concentration in fishes in Finland",
    x="PFAS concentration (ng/g fresh weight)",
    y="Cumulative probability"
  )

# The code may produce some negative values, which are removed from the graph
ggsave("PFAS-pitoisuus kalassa Suomessa.svg")
## Saving 7 x 5 in image
ggplot(conc@output, aes(x=concResult, colour=Fish))+stat_ecdf()+
  facet_wrap(~Compound, scales="free_x")

#ggplot(oapply(expo_dir, cols=c("Iter"),FUN=mean)@output,
#       aes(x=Gender, weight=expo_dirResult,fill=Fish))+geom_bar()+
#  facet_wrap(~Compound, scales="free_y")+
#  labs(title="Eri yhdisteiden saanti kalasta")
#ggsave("Yhdisteiden saanti kalasta Suomessa.svg")

plot_ly(trim(exposure), x=~Gender, y=~exposureResult, color=~Exposure_agent, text=~Exposure_agent, type="bar") %>%
  layout(yaxis=list(title="Exposure to nutrients (g or ug /d)"))
ggplot(trim(exposure), aes(x=Gender, weight=exposureResult, fill=Gender))+geom_bar()+
  facet_wrap(~Exposure_agent, scales="free_y")+
  labs(
    title="Exposure to compounds",
    y="(omega: mg/d; vit D: ug/d, PFAS: ng/d)"
  )

cat("Kalaperäisiä tautitaakkoja Suomessa\n")
## Kalaperäisiä tautitaakkoja Suomessa
if(openv$N>1) {
  tmp <- summary(oapply(BoDattr,NULL,sum,c("Age","Gender","Response")))
  tmp <- data.frame(
    Altiste = tmp$Exposure_agent,
    Vaikutus = tmp$Response,
    Keskiarvo = as.character(signif(tmp$mean,2)),
    "95 luottamusväli" = paste0(signif(tmp$Q0.025,2)," - ", signif(tmp$Q0.975,2)),
    Keskihajonta = signif(tmp$sd,2)
  )#[rev(match(lev, tmp$Exposure_agent)),]

  oprint(tmp)
  
  tmp <- summary(oapply(BoDattr,NULL,sum,c("Age","Gender","Exposure_agent")))
  tmp <- data.frame(
    Terveysvaikutus = tmp$Response,
    Keskiarvo = signif(tmp$mean,2),
    "95 luottamusväli" = paste0(signif(tmp$Q0.025,2)," - ", signif(tmp$Q0.975,2)),
    Keskihajonta = signif(tmp$sd,2)
  )
  oprint(tmp)
}

ggplot(trim(BoDattr), aes(x=Exposure_agent, weight=BoDattrResult, fill=Response))+geom_bar()

ggplot(trim(BoDattr), aes(x=Response, weight=BoDattrResult, fill=Exposure_agent))+geom_bar()

ggplot(trim(BoDattr), aes(x=Age, weight=BoDattrResult, fill=Response))+geom_bar(position="stack")

plot_ly(trim(BoDattr), x=~Exposure_agent, y=~BoDattrResult, color=~Response, text=~paste(Age, Exposure_agent, sep=": "), type="bar") %>%
  layout(yaxis=list(title="Disease burden (DALY /a); CHD2=coronary heart disease"), barmode="stack")
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
################ Insight network
gr <- scrape(type="assessment")
objects.latest("Op_en3861", "makeGraph") # [[Insight network]]
## Loading objects:
##   makeGraph
gr <- makeGraph(gr)
## Loading required package: DiagrammeR
## Loading objects:
##   formatted
## Loading objects:
##   chooseGr
#export_graph(gr, "ruori.svg")
#render_graph(gr) # Does not work: Error in generate_dot(graph) : object 'attribute' not found
##################### Diagnostics
objects.latest("Op_en6007", code_name="diagnostics")
## Loading objects:
##   showind
##   binoptest
##   showLoctable
##   ovashapetest
showLoctable()
tmp <- showind()
## Hg is not an ovariable.
## subgrouping is not an ovariable.
## mc2d is not an ovariable.
## sumExposcen is not an ovariable.
## mc2d is not an ovariable.
## mc2dparam is not an ovariable.
## TEFversion is not an ovariable.